Extrapolating trends in trust scores

ABSTRACT

Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity&#39;s trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

BACKGROUND

Trust is an essential component to many social and businessinteractions, but trust can be both hard to measure and difficult toquantify. People typically looks towards a variety of different factors,experiences, and influences to determine how much to trust another partyor entity in a transaction. For example, a potential customer decidingwhether to dine at a particular restaurant may take into account howmany times he or she has eaten at the restaurant, word of mouth fromfriends and family, and any ratings from online feedback sites. Asanother example, a bank may look up the credit score of a potentialborrower as a measure of their financial responsibility when determiningwhether to issue a loan. Often, people can have wildly differentpreferences as to which factors are the most important in determiningtrust levels, and these preferences may change depending on the type anddetails of the transaction. Trust can also change over time, reflectingthe cumulative experiences, transaction history, and recent trendsbetween entities. A single negative event can destroy trust, and trustcan also be rebuilt over time. All of the above considerations make“trust” an elusive measure to capture.

SUMMARY

Systems, devices, and methods are described herein for calculating atrust score. The trust score may be calculated between entitiesincluding, but not limited to, human users, groups of users, locations,organizations, or businesses/corporations and may take into account avariety of factors, including verification data, network connectivity,publicly available information, ratings data, group/demographicinformation, location data, and transactions to be performed, amongothers. The trust score may reflect the trustworthiness, reputation,membership, status, and/or influence of the entity in a particularcommunity or in relation to another entity. The trust score may takeinto account data from any suitable data sources, including, but notlimited to, network connectivity information, social networkinformation, credit score, available court data, opt-in provided data,transaction history, ratings/feedback data, group/demographics data,search engine data, or any publically available information. The trustscore may also include certain non-publically available informationprovided by the entities themselves (e.g., non-public transactionhistory, targeted ratings, etc.). In some embodiments, the trust scoremay also be calculated based on “crowdsourced” information. As usedherein, the term “crowdsource” means to receive input from a pluralityof other entities. For example, in addition to the data sourcesdiscussed above, users may provide and/or comment on attributes,characteristics, features, or any other information about another user.The participation of the “crowd” may form a type of validation for theinformation and give comfort to second-order users, who know that themembers of the crowd can spectate and make contributions to theattributes, characteristics, features, and other information. Toillustrate, a user may indicate that another entity is a good plumber.Many other users may provide a “thumbs up” to this attribute and/orprovide comments about their experiences with the entity, indicatingthat they too think that the user is a good plumber. These types ofinputs and comments may be integrated into the calculation of a trustscore for the user, thereby integrating the opinion of the “crowd” intothe trust score.

As used herein, a “system trust score” refers to a trust scorecalculated for an entity based on information available for the entity,without specific reference to another entity or activity/transaction.The system trust score may represent a base level of trustworthiness forthe entity that does not take into account information about a specificactivity/transaction. In some embodiments, the system trust score may becalculated based on publicly available information, such as verificationdata, a network connectivity score, and/or ratings data. As definedherein, a “network community” may include any collection or group ofentities connected through a network, including, but not limited to acomputer network or a social network. In some embodiments, a user mayset an initial trust score as a minimum trust level. In theseembodiments, the initial trust score may be retrieved and updated basedon publicly available information in order to determine the system trustscore. In some embodiments, the system trust score may be provided to anend user upon request without the end user having to identifythemselves. For example, an end user may query the system trust scoresof other entities, for example through a website or a mobileapplication, without having to sign into the website or mobileapplication or otherwise having to identify themselves. The system trustscore may also be calculated based on privately available informationand/or crowdsourced information. For instance, as discussed above, otherusers may provide attributes, characteristics, features, or otherinformation about a user, and that information may be integrated intothe system trust score.

As used herein, a “peer trust score” refers to a trust score calculatedfor a first entity in relation to a second entity. The peer trust scoremay take into account certain information that is specific to the firstand second entity, such as specific transaction history between thefirst and second entity, number of common contacts/friends, etc. In someembodiments, the peer trust score may be derived from the system trustscore and represent an update of the system trust score. For example, insome embodiments, the peer trust score may be calculated based onsubstantially the same data sources as the system trust score, wheresome components may be updated in order to further weight or take intoaccount additional information that is specific to the first and secondentity. In other embodiments, the peer trust score may be calculatedindependently from the system trust score and may be based on adifferent set of data sources than the system trust score.

In some embodiments, the peer trust score for the first entity may becalculated based on crowdsourced information that either validates datafrom one of the data sources used to calculate the system trust score orthat provides additional data that was not available from the datasources used to calculate the system trust score. In such instances, therelationships between the entities who provided the crowdsourcedinformation and the second user may provide valuable insight into thetrust between the first user and the second user. As an illustrativeexample, the first entity may have the attribute “trustworthy” listed onhis profile, which has a large number of “likes” by other users. Thesecond entity may be looking to enter a business transaction with thefirst entity and seek to calculate a peer trust score between the firstentity and the second entity. The peer trust score may take into accountthat some of the users that “liked” the attribute “trustworthy” for thefirst user are also friends of the second user in a social medianetwork. Thus, the calculation of the peer trust score is not only basedon the determination that the first entity is “trustworthy” according toa large number of other users, but the fact that some of those users arealso friends of the second user, whom the second user may trust morethan the opinion of strangers.

As used herein, a “contextual trust score” refers to a trust scorecalculated for a first entity in relation to a specific activity ortransaction. The contextual trust score may take into account certaininformation that is particular to the specific activity or transaction.In some embodiments, the contextual trust score may be derived from thesystem trust score or the peer trust score and represent an update ofthe system trust score or the peer trust score. For example, in someembodiments, the contextual trust score may be calculated based onsubstantially the same data sources as the system trust score, wheresome components may be updated in order to take into account informationthat is particular to the activity/transaction. In other embodiments,the contextual trust score may be calculated based on a different set ofdata sources than the system trust score and the peer trust score. Insome embodiments, the contextual trust score may be calculated byweighting data from different data sources based on the type ofactivity/transaction. For example, the trust score of a potentialborrower who is seeking a mortgage from a bank may heavily weight theborrower's credit score and financial history rather than their level ofconnectivity in a social network. In this manner, the contextual trustscore may be based on the same or similar data sources as the systemtrust score and/or the peer trust score, but with a different weightingto combine the data from the data sources. In some embodiments, specificdetails of the transactions may also affect the calculation of thecontextual trust score. For instance, the contextual trust score for afriend borrowing $10 may focus more on social network connectivity(e.g., the number of friends they have in common, etc.), while thecontextual trust score for a borrower seeking a $100K loan from the bankmay focus more on financial factors. In some embodiments, the details ofthe transaction may affect the weighting of the combination of data fromthe data sources.

In some embodiments, the contextual trust score may be based oncrowdsourced information, similar to the system trust score and the peertrust score described above. For instance, other users may provideattributes, characteristics, features, or other information about auser. These attributes, characteristics, features, or other informationmay be particularly relevant for certain transaction types. Forinstance, extending the illustrative example from above, the borrowermay have the attribute “financially responsible,” validated by 100people, which affects the lender's decision whether to lend money to theborrower. In this manner, once a transaction type is identified for usein calculating a contextual trust score, relationships between thetransaction type and one or more attributes associated with the user maybe identified, and the contextual trust score may be updated in view ofthese relationships.

According to one aspect, a method for updating a trust score maycomprise identifying paths from a first entity to a second entity,calculating a network connectivity score based on the identified paths,receiving data about the second entity from a remote source, andcalculating a ratings score based on the received data from the remotesource. A trust score for the second entity may be determined bycombining the network connectivity score and the ratings score. Anindication of an activity to be performed by the first entity and thesecond entity may be received, and the trust score may be updated basedon the indication of the activity. In some embodiments, the first andsecond entity may be connected by a social network. In such embodiments,identifying paths from the first entity to the second entity maycomprise identifying an intermediate entity in the social network thatconnects the first entity to the second entity. For example, theintermediate entity may be a common friend between a first user and asecond user. Calculating the network connectivity score may comprisedetermining a number of mutual friends between the first entity and thesecond entity. For example, the network connectivity score may beassigned according to a graduated scale based on the number of mutualfriends between the first entity and the second entity. The networkconnectivity score may also be calculated based on the number ofidentified paths between the first and the second entity and whether thenumber of identified paths exceeds a certain threshold.

In some embodiments, the ratings data may be one of a credit score,criminal history data, financial transaction history data, and/orbusiness reviews data. The ratings data may be combined with the networkconnectivity score according to a weighted combination, such as aweighted average, weighted sum, or other suitable weighting function, inorder to determine the trust score for the second entity. The weightedcombination may be based on a default set of weights or based onuser-assigned weights. The trust score for the second entity may then beupdated based on the indication of the activity. For example, theindication of the activity may adjust the weighted combination such thata different weighted combination is used to calculate the trust scorefor the second entity.

In some embodiments, a default set of weights for calculating a system,peer, or contextual trust score may be provided by a systemadministrator. In some embodiments, the default set of weights may becustomized both to individual entities as well as to individualtransaction/activity types. To determine an entity's trust model and/orrisk tolerance, any of the data source described above herein,including, for example, network connectivity, credit score or financialinformation, court data, transaction history, search engine mining,ratings/feedback data, or group/demographics data, may be gathered andsearched. In some embodiments, an entity's past transactions may besearched to identify a pattern for certain types of transactions. Forinstance, the entity may only enter into such transactions if the user'strust score is above a certain value. Identifying patterns may compriseestimating a set of virtual weights for each past transaction and takingan average of all of the estimated sets. In this manner, the system may“guess” how the entity is combining data from different data sources andwhich data sources it prefers for certain transaction types. Byestimating the weights for each of the entity's past transactions, thesystem may estimate, over time, the entity's trust model and provide amore accurate set of weights for future transactions of the same type.In some embodiments, in response to the user indicating that it wishesto calculate a trust score for a certain transaction type, the systemmay provide the estimated set of weights based on its determination ofthe entity's trust model and risk tolerance.

In some embodiments, the system or a system administrator(s) may developdefault weighting profiles for different transaction types based on howa plurality of entities in the computer network are adjusting weights.For example, the system may store weighting profiles for certaintransaction types that have been adjusted by entities. The system maycalculate, for each weight in a set of weights of the weighting profile,a difference or delta from a default set of weights. The system may takean average of these delta values to determine, on average, how entitiesare changing the weightings. The system may apply these delta values tothe current default set of weights to produce an updated default set ofweights. The system may then propagate the updated default set ofweights to the plurality of entities in the computer network. In thismanner, the system may keep up with general trends of the populationwith regards to trust models and risk tolerance.

In some embodiments, at least one of the first entity and the secondentity is a human user. For instance, the trust score may be calculatedbetween two users who are participating in a certain activity. Inanother embodiment, at least one of the first entity and the secondentity may be a business. For example, the trust score between a userand a restaurant may be calculated in order to aid the user indetermining whether to eat at the restaurant. In yet other embodiments,at least one of the first entity and the second entity may be a group ofusers or an organization. As an illustrative example, the second entitymay be the Boy Scouts of America, and the trust score may be calculatedbetween a first user and the Boy Scouts of America. In some embodiments,at least one of the first and second entity may be a product or anobject. For instance, the first entity may be a first user, and thesecond entity may be a chainsaw, and a trust score may be calculatedbetween the chainsaw and the first user. In this example, the trustscore may take into account any user reviews of the chainsaw receivedfrom a third-party ratings source. In some embodiments, at least one ofthe first and second entity may be a location, city, region, nation, orany other geographic place. For instance, a trust score between a firstuser and a city, such as New York City, may be calculated. In thisexample, the trust score may take into account number of contacts thatthe first user has in New York City, traveler reviews received fromthird-party ratings sources, and/or and activities, transactions, orinteractions that the first user has had with New York City.

In some embodiments, a decision related to the activity may beautomatically resolved based, at least in part, on a calculated trustscore. For instance, a bank may request the trust score of a potentialborrower in order to evaluate the suitability of the borrower for aloan. Based on the updated trust score, the bank may automatically issuethe loan, for example, if the trust score exceeds a certain threshold.In this manner, the system trust score, peer trust score, and/or thecontextual trust score can, either alone or in combination, form thebasis for automatic decision making.

In some embodiments, at least one of the system, peer, and/or contextualtrust score may include a confidence range. For example, each of thecomponents from the data sources may comprise a confidence range (suchas a variance or a standard deviation) indicating a level of uncertaintyin the data, and the component scores may be combined to form one of thesystem, peer, and/or contextual trust score. Thus, the resulting trustscore may be represented by a mean score and a confidence range, and insome embodiments, the confidence range may be represented by a mean andstandard deviation.

According to another aspect, methods and systems for calculating a trustscore based on crowdsourced information are described herein. First dataassociated with a first entity in a computer network may be retrievedfrom a first database using processing circuitry. The first data may beretrieved from any local or remote data source, as described herein. Theprocessing circuitry may calculate a first component score based on thefirst data. The processing circuitry may also retrieve, from a seconddatabase, second data associated with the first entity and calculate asecond component score based on the second data. Using the firstcomponent score and the second component score, the processing circuitrymay produce a trust score for the first entity by calculating a weightedcombination, such as, for example, a weighted average of the firstcomponent score and the second component score. Although only twocomponent scores are discussed in this illustrative example, it will beunderstood by those of ordinary skill in the art that data may beretrieved from any number of data sources and that any number ofcomponent scores may be calculated and combined to produce the trustscore for the first entity.

The processing circuitry may receive, from a user device of a secondentity in the computer network, data indicating an attribute associatedwith the first entity. As used herein, an “attribute” includes anydescriptive information associated with the first entity. Attributes mayinclude, but are not limited to, characteristics, features, skills,employment information, behavioral indicators, opinions, ratings, groupmembership, or any other descriptive adjectives. The processingcircuitry may also receive data indicating an attribute associated withthe first entity from other sources, such as a remote database or from asystem administrator. In some embodiments, the first entity itself mayprovide the attribute using manual user input. As an illustrativeexample, an attribute for Bob may be “entrepreneur.” Bob may haveprovided this information himself, for instance, through a text input onhis mobile phone, and the attribute may be added to Bob's profile. Insome embodiments, someone else, such as Bob's friend, may have indicatedthat Bob is an entrepreneur using his or her own mobile phone. In someembodiments, a system administrator may have recognized Bob as anentrepreneur, or the fact that Bob is an entrepreneur may have beenretrieved from a remote database (such as an employment database).Finally, the attribute may be automatically attributed or inferred ontothe entity by automatically recognizing related data received from othersources. For instance, data received from certain databases may berelated to certain attributes, and, in these instances, the attributemay automatically be assigned to an entity. As an illustrative example,data received from court databases may include an entity's criminalhistory for a certain period of time. If the data received indicatesthat a certain individual was convicted of a felony, then the attribute“criminal” may be automatically added to the individual's profile. Insome embodiments, these relationships between attributes and receiveddata may be set by a system administrator. For instance, the systemadministrator may set a trigger such that the receipt of the criminalhistory data indicating that the individual has committed a felon withinthe last five years may cause the attribute “criminal” to beautomatically added to the individual's profile.

In some embodiments, the second entity may enter a user input eithervalidating or disagreeing with an attribute. For instance, the secondentity may validate or disagree with the attribute by selecting auser-selectable icon, such as a thumbs up/down icon, like/dislike icon,plus/minus icon, positive/negative icon, or other such indicators. Otherconfigurations are contemplated, including inputting zero to five starsor indicating a score of 0 to 10 based on how much they agree with theattribute. Such user inputs may be received from a plurality of entitiesin a computer network. In some embodiments, other users may also commenton the attribute. As an illustrative example, Bob may be associated withthe attribute “entrepreneur,” and 37 users may agree by selecting a“like” icon, and two users may disagree by selecting a “dislike” icon.In some embodiments, the two users who disliked the attribute may leavecomments to explain why they disagree with the attribute “entrepreneur”for Bob.

As mentioned above, the attributes may be connected with data used tocalculate component scores and trust scores. In some embodiments, theattributes and the feedback on the attributes from the users (the“crowdsourced” information) may be used to update the component scoresand/or the trust scores. In some embodiments, the attribute and/or ascore associated with the attribute may be used as a component scoreitself for the calculation of a trust score, as discussed in furtherdetail below. It will be understood from those of ordinary skill in theart that the component scores and the trust scores may be updated usingany suitable technique. In some embodiments, a net attribute score maybe calculated based on the received feedback from other users. Forinstance, the net attribute score may be calculated by finding adifference between the number of “thumbs up” and the number of “thumbsdown.” This difference may serve as an indicator of whether people agreewith the attribute (which should result in a positive net attributescore) or disagree with the attribute (which should result in a negativenet score). In some embodiments, such as embodiments that allow otherusers to rate using a star or numeric system, an average of the ratingsprovided by others users may be calculated for the net attribute score.

In embodiments where the attribute is related to a specific componentscore, the attribute may serve to increase or decrease the componentscore. As an illustrative example, the attribute “criminal” may relateto the court history component score used to calculate a system trustscore. In such embodiments, processing circuitry may calculate the courthistory component score as described herein, and adjust the courthistory component score based on a determination that the entity is alsoattributed with the “criminal” attribute. In some embodiments, thisadjustment may be a multiplier based on the net attribute score. Inother embodiments, the adjustment may add a certain number of percentagepoints to the component score. In some embodiments, the adjustment maybe limited by a threshold adjustment. That is, even if the multiplierand/or percentage adjustment exceeds the threshold adjustment, thethreshold adjustment will serve as a maximum adjustment for thecomponent score. As an illustrative example, a court history componentscore may comprise 100 points out of a total 1000 points for an overalltrust score. Based on court history data retrieved from a courtdatabase, processing circuitry may calculate a court history componentscore of 60 out of the 100 points for Sam. However, Sam is alsoassociated with the “criminal” attribute. In such embodiments, theprocessing circuitry may automatically adjust the court historycomponent score down by 10%, resulting in an adjusted court historycomponent score of 54. In some embodiments, the maximum amount that anattribute (or a collection of attributes) may affect a component scoremay be 5 points. In such embodiments, the adjusted court historycomponent score may be 55, because the calculated adjustment of 6 islimited by the threshold value of 5. In this manner, the magnitude ofadjustment that an entity's attributes may have on its component and/ortrust scores may be limited by these thresholds.

In some embodiments, the adjustment to a component and/or trust scorecaused by an attribute may be based on a distribution of net attributescores for entities with the attribute. For example, Mike the musicianmay have 1,000 “likes” for the attribute “guitarist” on his profile.However, the average “likes” for the attribute “guitarist” may be onemillion. Compared to all of the guitarists in the world, Mike's 1,000“likes” may make him a relatively unknown musician. On the other hand,Phil the philanthropist may have 100 “likes” for the attribute“philanthropist” on his profile, which may place him in the top 1% forentities with that attribute. Thus, Phil the philanthropist may receivea larger multiplier to his trust score than Mike the musician, eventhough Mike the musician has a higher number of likes for his attribute.In this fashion, processing circuitry may identify a subgroup ofentities who are also associated with the attribute and calculate anappropriate adjustment based on the distribution of net attribute scoresamong the subgroup. In some embodiments, the processing circuitry maycalculate an average net attribute score for the subgroup. In otherembodiments, the processing circuitry may determine a distribution, suchas a Gaussian distribution, using the net attribute scores for thesubgroup. For example, the processing circuitry may determine thatphilanthropists, on average, receive about 30 “likes” for the attribute“philanthropist,” with a standard deviation of about 15. This wouldplace Phil the philanthropist's 100 “likes” several standard deviationsabove the average number of “likes” for a philanthropist. Based on theaverage net attribute score and/or the distribution, the processingcircuitry may calculate an appropriate adjustment to the relatedcomponent score. In some embodiments, the processing circuitry mayconsult a table or an equation which determines the relationship betweenthe net attribute score (for instance, 57 “likes”−12 “dislikes”=netattribute score of 45) and a multiplier for the component score (forinstance, a net attribute score between 40 to 50 gets a 1% multiplier tothe component score). In some embodiments, the adjustment may be applieddirectly to the trust score. For example, anyone with the“philanthropist” attribute may automatically receive an increase of fivepoints to their trust score out of 1000.

In some embodiments, any one or more of the attribute, net attributescore, adjustment to the component score, adjusted component score, orrecalculated trust score may be generated for display on a user device.For example, a user device may display the attribute “philanthropist”with an indication of “+5” next to the attribute, showing that theattribute caused an increase of +5 to the recalculated trust score.

In some embodiments, a third entity in the computer network may requestthe trust score for the first entity. The processing circuitry mayretrieve data indicating paths in the computer network and identify,based on the retrieved data indicating paths in the computer network, apath connecting the third entity to the second entity in the computernetwork. In some embodiments, the processing circuitry may identify onlypaths that comprise a number of links less than a threshold number. Theprocessing circuitry may adjust the component score and/or the trustscore based on the determination that the second and third entities areconnected by the identified path. In this manner, the processingcircuitry may not only adjust the component/trust scores based on theattribute, but also based on the identities of the entities who arevalidating or disagreeing with the attribute. For example, the fact thata close friend gave a thumbs up to the attribute “babysitter” may causea greater adjustment to a trust score than if a stranger had given athumbs up to the same attribute. Therefore, when calculating peer trustscores for a target entity in relation to a requesting entity, theprocessing circuitry may take into account the relationships between therequesting entity and those entities that validated or disagreed withthe target entity's attributes.

In some embodiments, crowdsourced information may also be used inconjunction with information that an activity will be performed in thefuture by a first entity and a second entity. For instance, an attributemay be associated with certain transaction types, and the fact that anentity is associated with the attribute may further adjust the componentscore and or trust score. As an illustrative example, the attribute“banker” may cause an increase in the contextual trust score for anyentity who is entering a financial transaction with the entity. In suchcases, the processing circuitry may, in addition to the adjustmentsdescribed above, calculate further adjustments to a component and/ortrust score based on the attributes. The relationships betweenattributes and component scores and/or transaction types may be providedby other entities, by system administrators, or be retrieved fromrelevant databases.

According to another aspect, systems and methods for updating a trustscore calculation algorithm are described herein. Processing circuitrymay transmit a weighting profile to a first user device and a seconduser device. The weighting profile may comprise a first set of weightsfor combining data from each of a plurality of data sources to calculatea trust score. The processing circuitry may receive a first user inputfrom the first user device to adjust the first set of weights to asecond set of weights that is different than the first set of weights.The processing circuitry may further receive a second user input fromthe second user device to adjust the first set of weights to a third setof weights that is different than the first set of weights. Based on thefirst user input and the second user input, the processing circuitry mayupdate the weighting profile to comprise a fourth set of weights forcombining data from each of a plurality of data sources to calculate atrust score, the fourth set of weights being different from the firstset of weights. For example, the processing circuitry may take anaverage of the second set of weights and the third set of weights. Theprocessing circuitry may transmit the updated weighting profile to athird user device that is different than the first user device and thesecond user device. In this manner, the processing circuitry may monitorchanges that entities are making to a default weighting profile, updatethe default weighting profile based on these changes, and propagate theupdated default weighting profile back to the entities.

In some embodiments, updating the weighting profile comprisescalculating a first difference between the first set of weights and thesecond set of weights, calculating a second difference between the firstset of weights and the third set of weights, and calculating an averagedifference from the first difference and the second difference. Theprocessing circuitry may then apply the average difference to the firstset of weights to produce the fourth set of weights. In someembodiments, the processing circuitry may cause the third user device tocalculate a trust score based on the updated weighting profile using thefourth set of weights.

In some embodiments, the set of weights in the weighting profile maycomprise percentages that are intended to add up to 100 percent. In suchembodiments, an increase of one weight may require a decrease in one ormore other weights in the set of weights to maintain a total sum of 100percent. The processing circuitry may automatically adjust weights tomaintain this sum of weights to equal 100 percent. In some embodiments,after the processing circuitry applies the average difference to thefirst set of weights, the processing circuitry may sum the updated setof weights and normalize each weight in the updated set of weights bydividing each weight in the updated set of weights by the sum. In thismanner, even if the updated set of weights sums to more or less than 100percent, the processing circuitry may normalize the set of weights tosum to 100 percent.

According to another aspect, systems and methods for updating a trustscore based on extrapolated trends are described herein. Processingcircuitry may retrieve from a database a first trust score associatedwith a first entity in a computer network, wherein the first trust scorewas calculated at a first time. The processing circuitry may determinethat the first trust score has not been updated for the first entity fora threshold period of time. For example, the processing circuitry maydetermine that a difference between the first time and a current timeexceeds the threshold period of time. In response to determining thatthe difference between the first time and the current time exceeds thethreshold period of time, the processing circuitry may identify a secondentity in the computer network and retrieve a second trust scorecalculated at a second time and a third trust score calculated at athird time, wherein the second trust score and the third trust score areassociated with the second entity. The second entity may have a trustscore that was calculated later than the first trust score for the firstentity, and, thus, the second entity may be a suitable indicator oftrends in trust scores since the time that the first trust score wascalculated. For example, the processing circuitry may determine that atleast one of the second time or the third time is later than the firsttime, indicating that the second entity has a trust score that wascalculated later than the first trust score for the first entity.

The processing circuitry may calculate a trend using the second trustscore and the third trust score. Although only two trust scores arediscussed in this illustrative example, it will be understood by thoseof ordinary skill in the art that the trend may be based on any two ormore trust scores associated with the second entity. In someembodiments, the processing circuitry may also calculate trends in oneor more component scores used to calculate the trust scores. The trendmay comprise, for example, a general slope of increasing trust score ordecreasing trust score over time. The trend for the second entity may beindicative of how the trust score for the first entity should changeover a similar period of time. The processing circuitry may update thefirst trust score using the calculated trend. For example, theprocessing circuitry may apply the slope of increasing or decreasingtrust score to the first trust score over the same period of time as thetrend of the trust scores of the second entity.

In some embodiments, the processing circuitry may apply a trend toindividual component scores, update the individual component score, andrecalculate the trust score for the first entity. As an illustrativeexample, credit scores for a plurality of entities may have decreased by10% in the past two years. The processing circuitry may identify thistrend by analyzing the retrieved credit scores of a plurality ofentities. The processing circuitry may then utilize this trend to updatea credit score component score for an entity for which it does not haveupdated credit score data and recalculate a trust score for the entitybased on the updated credit score component score. In this manner, theprocessing circuitry may update trust scores for entities for whichupdated data is not available.

In some embodiments, calculating a trend comprises one of: calculating adifference between the second trust score and the third trust score,calculating a difference per time between the second trust score and thethird trust score, calculating a percent increase/decrease from thesecond trust score to the third trust score, or calculating a percentincrease/decrease per time from the second trust score to the thirdtrust score.

In some embodiments, the processing circuitry may identify trends intrust scores only for entities that are connected to the first entity.For example, the processing circuitry may identify a subset of entitiesthat are connected to the first entity in a computer network by at leastone path that is fewer than a threshold number of links. Thus, thetrends in trust scores may be determined based on entities that arerelated to the first entity.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, and in which:

FIG. 1 is a block diagram of an illustrative architecture forcalculating a trust score;

FIG. 2 is another block diagram of an illustrative architecture forcalculating a trust score;

FIG. 3 is a diagram of an illustrative tiered trust score system;

FIG. 4 is a block diagram of illustrative components that comprise asystem trust score;

FIG. 5 is a diagram of an illustrative weighted combination ofcomponents that comprise a system trust score;

FIG. 6 is an illustrative graphical user interface displaying a trustscore interface;

FIG. 7 is a graphical user interface displaying another illustrativetrust score interface;

FIG. 8 is a table showing an illustrative graded scale for assigningcomponent scores based on a metric;

FIG. 9 is an illustrative distribution for assigning component scoresbased on a metric;

FIG. 10 is a display of an illustrative network graph;

FIG. 11 is an illustrative data table for supporting connectivitydeterminations within a network community;

FIG. 12 is another illustrative data table for supporting connectivitydeterminations within a network community;

FIGS. 13A-E are illustrative processes for supporting connectivitydeterminations within a network community;

FIG. 14 is an illustrative process for calculating a system trust score;

FIG. 15 is an illustrative process for calculating a peer trust score;

FIG. 16 is an illustrative process for calculating a contextual trustscore;

FIG. 17 is an illustrative process for adjusting weighting profilesbased on user inputs;

FIG. 18 is an illustrative display for providing attributes associatedwith an entity;

FIG. 19 is an illustrative process for calculating a system trust scorebased on attributes associated with an entity;

FIG. 20 is an illustrative process for calculating a peer trust scorebased on attributes associated with an entity;

FIG. 21 is an illustrative process for calculating a contextual trustscore based on attributes associated with an entity; and

FIG. 22 is an illustrative process for updating a trust score based onextrapolated trends.

DETAILED DESCRIPTION

To provide an overall understanding of the systems, devices, and methodsdescribed herein, certain illustrative embodiments will be described. Itwill be understood that the systems, devices, and methods describedherein may be adapted and modified for any suitable application and thatsuch other additions or modifications will not depart from the scopehereof.

FIG. 1 shows a block diagram of an architecture 100 for calculating atrust score in accordance with certain embodiments of the presentdisclosure. A user may utilize access application 102 to accessapplication server 106 over communications network 104. For example,access application 102 may include a computer application such as astandard web browser or an app running on a mobile device. Applicationserver 106 may comprise any suitable computer server, including a webserver, and communication network 106 may comprise any suitable network,such as the Internet. Access application 102 may also includeproprietary applications specifically developed for one or moreplatforms or devices. For example, access application 102 may includeone or more instances of an Apple iOS, Android, or WebOS application orany suitable application for use in accessing application server 106over communications network 104. Multiple users may access applicationserver 106 via one or more instances of access application 102. Forexample, a plurality of mobile devices may each have an instance ofaccess application 102 running locally on the respective devices. One ormore users may use an instance of access application 102 to interactwith application server 106.

Communication network 104 may include any wired or wireless network,such as the Internet, WiMax, wide area cellular, or local area wirelessnetwork. Communication network 104 may also include personal areanetworks, such as Bluetooth and infrared networks. Communications oncommunications network 104 may be encrypted or otherwise secured usingany suitable security or encryption protocol.

Application server 106, which may include any network server or virtualserver, such as a file or web server, may access data sources 108locally or over any suitable network connection. Application server 106may also include processing circuitry (e.g., one or more computerprocessors or microprocessors), memory (e.g., RAM, ROM, and/or hybridtypes of memory), and one or more storage devices (e.g., hard drives,optical drives, flash drives, tape drives). The processing circuitryincluded in application server 106 may execute a server process forcalculating trust scores, while access application 102 executes acorresponding client process. The access application 102 may be executedby processing circuitry on a user's equipment, such as a computer or amobile device (e.g., a cell phone, a wearable mobile device such as asmartwatch, etc.). The processing circuitry included in applicationserver 106 and/or the processing circuitry that executes accessapplication 102 may also perform any of the calculations andcomputations described herein in connection with calculating a trustscore. In some embodiments, a computer-readable medium with computerprogram logic recorded thereon is included within application server106. The computer program logic may calculate trust scores and maygenerate such trust scores for display on a display device. In someembodiments, application 102 and/or application server 106 may store acalculation date of a trust score and may generate for display the trustscore together with a date of calculation.

Application server 106 may access data sources 108 over the Internet, asecured private LAN, or any other communications network. Data sources108 may include one or more third-party data sources, such as data fromthird-party social networking services and third-party ratings bureaus.For example, data sources 108 may include user and relationship data(e.g., “friend” or “follower” data) from one or more of Facebook,MySpace, openSocial, Friendster, Bebo, hi5, Orkut, PerfSpot, Yahoo! 360,LinkedIn, Twitter, Google Buzz, Really Simple Syndication readers or anyother social networking website or information service. Data sources 108may also include data stores and databases local to application server106 containing relationship information about users accessingapplication server 106 via access application 102 (e.g., databases ofaddresses, legal records, transportation passenger lists, gamblingpatterns, political and/or charity donations, political affiliations,vehicle license plate or identification numbers, universal productcodes, news articles, business listings, and hospital or universityaffiliations).

Application server 106 may be in communication with one or more of datastore 110, key-value store 112, and parallel computational framework114. Data store 110, which may include any relational databasemanagement system (RDBMS), file server, or storage system, may storeinformation relating to one or more network communities. For example,one or more of data tables 1100 (FIG. 11) may be stored on data store110. Data store 110 may store identity information about users andentities in the network community, an identification of the nodes in thenetwork community, user link and path weights, user configurationsettings, system configuration settings, and/or any other suitableinformation. There may be one instance of data store 110 per networkcommunity, or data store 110 may store information relating to a pluralnumber of network communities. For example, data store 110 may includeone database per network community, or one database may storeinformation about all available network communities (e.g., informationabout one network community per database table).

Parallel computational framework 114, which may include any parallel ordistributed computational framework or cluster, may be configured todivide computational jobs into smaller jobs to be performedsimultaneously, in a distributed fashion, or both. For example, parallelcomputational framework 114 may support data-intensive distributedapplications by implementing a map/reduce computational paradigm wherethe applications may be divided into a plurality of small fragments ofwork, each of which may be executed or re-executed on any core processorin a cluster of cores. A suitable example of parallel computationalframework 114 includes an Apache Hadoop cluster.

Parallel computational framework 114 may interface with key-value store112, which also may take the form of a cluster of cores. Key-value store112 may hold sets of key-value pairs for use with the map/reducecomputational paradigm implemented by parallel computational framework114. For example, parallel computational framework 114 may express alarge distributed computation as a sequence of distributed operations ondata sets of key-value pairs. User-defined map/reduce jobs may beexecuted across a plurality of nodes in the cluster. The processing andcomputations described herein may be performed, at least in part, by anytype of processor or combination of processors. For example, varioustypes of quantum processors (e.g., solid-state quantum processors andlight-based quantum processors), artificial neural networks, and thelike may be used to perform massively parallel computing and processing.

In some embodiments, parallel computational framework 114 may supporttwo distinct phases, a “map” phase and a “reduce” phase. The input tothe computation may include a data set of key-value pairs stored atkey-value store 112. In the map phase, parallel computational framework114 may split, or divide, the input data set into a large number offragments and assign each fragment to a map task. Parallel computationalframework 114 may also distribute the map tasks across the cluster ofnodes on which it operates. Each map task may consume key-value pairsfrom its assigned fragment and produce a set of intermediate key-valuepairs. For each input key-value pair, the map task may invoke auser-defined map function that transmutes the input into a differentkey-value pair. Following the map phase, parallel computationalframework 114 may sort the intermediate data set by key and produce acollection of tuples so that all the values associated with a particularkey appear together. Parallel computational framework 114 may alsopartition the collection of tuples into a number of fragments equal tothe number of reduce tasks.

In the reduce phase, each reduce task may consume the fragment of tuplesassigned to it. For each such tuple, the reduce task may invoke auser-defined reduce function that transmutes the tuple into an outputkey-value pair. Parallel computational framework 114 may then distributethe many reduce tasks across the cluster of nodes and provide theappropriate fragment of intermediate data to each reduce task.

Tasks in each phase may be executed in a fault-tolerant manner, so thatif one or more nodes fail during a computation the tasks assigned tosuch failed nodes may be redistributed across the remaining nodes. Thisbehavior may allow for load balancing and for failed tasks to bere-executed with low runtime overhead.

Key-value store 112 may implement any distributed file system capable ofstoring large files reliably. For example, key-value store 112 mayimplement Hadoop's own distributed file system (DFS) or a more scalablecolumn-oriented distributed database, such as HBase. Such file systemsor databases may include BigTable-like capabilities, such as support foran arbitrary number of table columns.

Although FIG. 1, in order to not over-complicate the drawing, only showsa single instance of access application 102, communications network 104,application server 106, data source 108, data store 110, key-value store112, and parallel computational framework 114, in practice architecture100 may include multiple instances of one or more of the foregoingcomponents. In addition, key-value store 112 and parallel computationalframework 114 may also be removed, in some embodiments. As shown inarchitecture 200 of FIG. 2, the parallel or distributed computationscarried out by key-value store 112 and/or parallel computationalframework 114 may be additionally or alternatively performed by acluster of mobile devices 202 instead of stationary cores. In someembodiments, cluster of mobile devices 202, key-value store 112, andparallel computational framework 114 are all present in the networkarchitecture. Certain application processes and computations may beperformed by cluster of mobile devices 202 and certain other applicationprocesses and computations may be performed by key-value store 112 andparallel computational framework 114. In addition, in some embodiments,communication network 104 itself may perform some or all of theapplication processes and computations. For example, speciallyconfigured routers or satellites may include processing circuitryadapted to carry out some or all of the application processes andcomputations described herein.

Cluster of mobile devices 202 may include one or more mobile devices,such as PDAs, cellular telephones, mobile computers, or any other mobilecomputing device. Cluster of mobile devices 202 may also include anyappliance (e.g., audio/video systems, microwaves, refrigerators, foodprocessors) containing a microprocessor (e.g., with spare processingtime), storage, or both. Application server 106 may instruct deviceswithin cluster of mobile devices 202 to perform computation, storage, orboth in a similar fashion as would have been distributed to multiplefixed cores by parallel computational framework 114 and the map/reducecomputational paradigm. Each device in cluster of mobile devices 202 mayperform a discrete computational job, storage job, or both. Applicationserver 106 may combine the results of each distributed job and return afinal result of the computation.

FIG. 3 is a diagram 300 of a tiered trust score system in accordancewith certain embodiments of the present disclosure. The system trustscore 302, peer trust score 304, and contextual trust score 306 mayrepresent a tiered trust system in which a user may inquire about thetrustworthiness of a target entity either in isolation, in relation toanother entity, and/or in relation to a specific activity/transaction.In some embodiments, the system trust score 302 may be calculated from afirst set of data sources, (e.g., data sources 108 in FIG. 1). In someembodiments, the peer trust score 304 may be calculated as an update tosystem trust score 302 based on a second set of data sources, which mayor may not be the same as the first set of data sources. Peer trustscore 304 may or may not take into account additional data sources(e.g., data sources 108 in FIG. 1). In some embodiments, peer trustscore 304 may also combine the data from the data sources according to adifferent weighting than the system trust score 302. In someembodiments, the contextual trust score 306 may be calculated as anupdate to either peer trust score 304 or system trust score 302. Forexample, the contextual trust score 306 may take into account differentdata sources (e.g., data sources 108 in FIG. 1) or may be based on thesame data sources as system trust score 302 and/or peer trust score 304.In some embodiments, the contextual trust score 306 may combine datafrom the data sources according to a different weighting as system trustscore 304 and/or peer trust score 304. Although the system trust score302, peer trust score 304, and contextual trust score 306 are shown inFIG. 3 as a hierarchical system, each trust score may be calculated andpresented either separately or together with the other trust scores.

The system trust score 302, peer trust score 304, and contextual trustscore 306 may be represented in any suitable fashion. As an illustrativeexample, the system trust score 302, peer trust score 304, andcontextual trust score 306 may each be represented as a percentage outof 100 or as a numerical score out of 1000. In other embodiments, thesystem trust score 302, peer trust score 304, and contextual trust score306 may be represented by different categories of trustworthiness (e.g.,“reliable,” “flaky,” “honest,” “fraudulent,” etc.) or by a graphicalscheme (e.g., a color spectrum representing level of trustworthiness).For ease of illustration, the trust score and component scores thatcomprise the trust scores will be discussed herein as numerical values.However, other methods of portraying a calculated trust score will becontemplated by those of ordinary skill in the art and will not departfrom the scope hereof.

Each type of trust score may combine data from data sources according toa specific weighting. For instance, a weighting for a system trust scoremay be set as:

-   -   Data Verification—5%    -   Network Connectivity—20%    -   Credit Score—15%    -   Court Data—10%    -   Ratings/Feedback Data—20%    -   Group/Demographics—5%    -   Search Engine Mining—5%    -   Transaction History—20%        In some embodiments, a user may adjust these default weightings        according to their preferences. For example, a user who values        network analytics (e.g., how many friends we have in common) may        assign a heavier weight, e.g., 25% to network connectivity,        while lowering the weight of credit score to 10%. Conversely, a        bank who cares very much about the credit score of its customers        may assign a heavier weight to credit score and discount network        connectivity.

In some embodiments, these default weightings may be set by a systemadministrator. In some embodiments, the default weightings may becustomized to a user and/or to a specific transaction type. The systemmay analyze data received from any of the above mentioned data sourcesto determine an entity's trust model and/or risk tolerance. For example,certain data may indicate that an entity is more or less risk averse andthat the weightings should be adjusted accordingly.

The default weightings may also be adjusted on an ongoing basis. Thesystem may receive adjusted weighting profiles from a plurality ofentities and combine the adjusted weighting profiles. As an illustrativeexample, a high number of entities may adjust the search engine miningpercentage to 10% while reducing the ratings/feedback data to 15%. Thesystem may adjust the default weightings to reflect these changedpercentages and redistribute the weighting profiles to the entities.

The following is an example that illustrates one application of a systemtrust score 302, peer trust score 304, and contextual trust score 306.It will be understood that the following is provided for illustrativepurposes only and that the systems, devices, and methods describedherein may be further adapted or modified.

John sees an ad at ABC Restaurant for a short order cook and is tryingto decide if he should apply. John opens an app on his mobile device andsearches for ABC Restaurant. The app shows there are multiple matches tothis search, but the nearest one is sorted to the top. After tapping onthe correct restaurant, the app shows the ABC Restaurant profile page.The ABC Restaurant profile page includes a system trust score for ABCRestaurant, which is calculated based in part on the ratings from threeblogs. John taps to see more details and sees a list of most recentblogs from bloggers. By tapping on individual blogs, he can read theactual article. He can also tap on the bloggers to see their profilepage in the app.

The system trust score for ABC Restaurant is also calculated based onprevious transactions where ABC Restaurant was the employer. John tapsto show a list of previous transactions, ratings of those transactions,and comments.

John taps on the social graph to see how he is connected to therestaurant through one or more networks (e.g., Facebook, MySpace,Twitter, LinkedIn, etc.). From the social graph he sees that Bob, themanager, is a friend of a friend. Based on the social graph data, theapp updates the system trust score to calculate a peer trust scorebetween John and ABC Restaurant. The peer trust score is better than thesystem trust score to indicate the incremental increase intrustworthiness based on the connections between John and Bob themanager. The app also displays Bob's system trust score, calculatedbased on publicly available information and a default weighting, andBob's peer trust score with respect to John, which also takes intoaccount the social graph data.

John decides to apply for the job. After an interview, Bob the manageris deciding whether or not to hire John as a short order cook. Bob usesthe app to search for John. There are multiple results for John, but Bobeventually finds him and taps on his entry. John's profile page displayshis system trust score, calculated based on publicly availableinformation (e.g., credit score, verification data, search enginemining, employment history, etc.) and a default weighting. Bob taps onthe social graph to see how he is connected to John. He discovers thatthey are connected through a friend of a friend. The app updates John'ssystem trust score based on the social network data to calculate a peertrust score between John and Bob, which is better than John's systemtrust score to indicate the incremental increase in trustworthiness dueto the connections between John and Bob. The app also shows averageratings from previous transactions where John was the employee. Bob tapsto show a list of transactions, which can be ordered into chronologicalorder and filtered by type of job. Bob also indicates to the app that hewishes to hire John as an employee. The app adjusts the weightings ofthe trust score to give a higher weight to the employee history ratherthan other components (such as credit score). The app uses the adjustedweightings to update the peer trust score to calculate the contextualtrust score, which represents John's trustworthiness as a potentialemployee.

After reviewing the information in the app, Bob has decided to hireJohn. From John's profile page, he taps on the Action icon and chooses“Hire”. The app prompts Bob to fill in relevant information such asposition, start date, annual salary, and vacation days per year. Afterconfirming the data, the transaction appears in Bob's Notification list,with the status of “Waiting for John . . . ” John receives anotification on his phone. He opens the app and sees a new transactionin his Notifications list. The app prompts John to confirm the detailsof his new job. John chooses to confirm, and Bob receives a notificationthat John has confirmed the transaction.

As illustrated in the above example, a user may request a system trustscore for another entity, which may then be subsequently refined into apeer trust score based on information specific to the parties involvedand into a contextual trust score based on the details of anactivity/transaction to be performed by the parties.

FIG. 4 is a block diagram 400 of components 404-418 that comprise asystem trust score 402 in accordance with certain embodiments of thepresent disclosure. The system trust score 402 may comprise a dataverification component 404, a network connectivity component 406, acredit score component 408, a court data component 410, aratings/feedback data component 412, a group/demographics component 414,a search engine mining component 416, and/or a transaction historycomponent 418. The components 404-418 may be received either locally orthrough a suitable network connection from one or more data sources(e.g., data sources 108 in FIG. 1). It will be understood thatcomponents 404-418 are provided for illustrative purposes only and thatthe trust scores described herein may comprise more or fewer componentsthan components 404-418 provided in FIG. 4.

Data verification component 404 may include data that verifiesinformation associated with the target entity. In some embodiments, thedata verification component 404 may include verification of contactinformation, including, but not limited to, email address, phone number,and/or mailing address. The data verification component may alsocomprise email, IM, and other messaging factors, such as frequency ofmessages, time of day of messages, depth of thread, or a review ofthreads for key transaction/activity types (e.g., loan, rent, buy,etc.). Data verification component 404 may take into account data frompassport and/or other government IDs, tax return factors (e.g., asummary of a tax return to prove income), educational data (e.g.,certificates of degree/diploma), group affiliation factors (e.g.,invoices that prove membership to a group), achievements (e.g., proof ofawards, medals, honorary citations, etc.), employment data (e.g.,paystub data). The data verification component 404 may also incorporatefacial recognition software to verify certain documents, such as IDs. Insome embodiments, this facial recognition software may be used forsubsequent verification of the user's identity. As an illustrativeexample, the data verification component 404 may be used as a part of anairport scanning system to verify the user's identity. The dataverification component 404 may comprise subcomponents such as datacorresponding to the above illustrative examples, and as moresubcomponents are verified, the higher the data verification component404. The subcomponents may be combined to determine the dataverification component 404 in any suitable manner, such as a weightedsum or the method discussed further below in relation to FIGS. 8 and 9.In some embodiments, verification of the data may be achieved by adocument that proves the subject of the subcomponent (e.g., a tax returnto prove income) or by peer verification. For instance, employmentinformation may be vetted by peers connected to the target user, and asmore peers positively vet the employment information, the better thesubcomponent score becomes. In some embodiments, the information may bedeleted once verified. For example, images of passports/IDs may bedeleted once the information contained therein is validated.

Network connectivity component 406 is discussed further below inrelation to FIGS. 11-13. In some embodiments, the network connectivitycomponent 406 may comprise data from a social network (e.g., Facebook,Twitter, Instagram, Pinterest, LinkedIn, etc.). For example, the networkconnectivity component 406 may take into account the number ofconnections, such Facebook “friends” that the target user has, thosefriends that comment or “like” the target user's posts, information onwho the target user adds/removes as a friend, duration of the targetuser's friends (e.g., how long after the user adds them as a friend doesthe target user remove them as a friend), who the target user messages,which posts the target user shares, and length of tenure on the socialnetwork. For a peer trust score, such as peer trust score 304, thenetwork connectivity component may take into account number of mutualfriends, degree of separation, and number of paths from a first entityto the target entity.

Credit score component 408 may comprise any suitable financialinformation associated with the target entity, including income,checking/savings account information (number of accounts, value), andcredit score information from one or more institutions. The credit scoreinformation may be received from any typical credit score agency,including, but not limited to, Transunion, Equifax, and Experian. Creditscore factors may also be taken into account, such as number of creditaccounts, credit utilization, length of credit history, number of latepayments, etc. Other financial information taken into account mayinclude prior loan and payment data, data on net worth orassets/liabilities, and information on any prior infractions. Thevarious financial data may be combined using any suitable approach,including, but not limited to, the methods discussed below in relationto FIGS. 8 and 9.

Court data component 410 may include any data on activity associatedwith the target entity in a criminal or civil court. For example, courtdata component 410 may comprise data on how many cases involve theentity suing someone else and the type of suit, how many cases involvethe target entity as the defendant, any criminal cases that may have anegative impact on trustworthiness, and the final holding/disposition ofany concluded cases (e.g., acquitted, convicted, settled, etc.). Courtdata may be derived from any publicly available sources and from anyavailable municipal, state, federal, or international court.

A ratings/feedback data component 412 may include any data that reflectsa rating or feedback associated with the target entity. For instance,online rating sites such as Yelp may provide ratings information onvarious businesses. Any ratings of the target entity, information onvolume, number of ratings, average rating, who rates the target entity,and whether the target entity responds to comments may be taken intoaccount. In some embodiments, ratings data may be received from ratingsinstitutions, such as the Better Business Bureau. Feedback data mayinclude any positive or negative comments associated with the targetentity. In some embodiments, feedback data may include comments made bypeers in a social network. In some embodiments, the number and timing ofratings by other users or entities may be used to affect theratings/feedback data component 412. For instance, a lack of negativefeedback for a specified period of time may result in an increase (ordecrease) in the ratings/feedback data component 412. Similarly, a lackof positive feedback for a specified period of time may result in adecrease (or increase) in the ratings/feedback data component 412.

Group/demographics component 414 may include information on groupmembership of the target entity or demographic information such as age,sex, race, location, etc. The group data may suggest an activityperformed by the target entity. For instance, membership to a nationalsailing club may indicate an interest in sailing and boats. In someembodiments, a peer trust score may be adjusted to take into account thegroup/demographic component. For instance, the peer trust score for atarget entity may be increased if a first entity and the target entityare both members of the same national sailing club. As another example,similarities in demographic information (age, sex, race, location, etc.)may indicate an incremental increase in trustworthiness between a firstand the target entity, and the peer trust score for the target entitymay be adjusted accordingly.

The search engine mining component 416 may include analytics performedon suitable search engines, such as Google or Yahoo.Websites/blogs/articles may be searched and scanned for entries aboutthe target entry and a positive or negative sentiment may be detectedand stored for such entries. Number of articles, sentiment, timing ofthe articles, may indicate a positive or negative adjustment to thesearch engine mining component 416. In some embodiments, online shoppingor auction websites such as eBay may be scanned for informationassociated with the target entity, such as rating and volume oftransactions, feedback comments, number of bought/sold items, averagevalue of items, and category of items (e.g., hardware, software,furniture, etc.).

Transaction history component 418 may comprise any information on pasttransactions associated with the target entity. Successful transactionsor activities may be identified and positively impact the transactionhistory component score. For example, if I loan John $100 and hepromptly pays me back, I may be more inclined to loan him money in thefuture. Transaction history data may be locally tracked and stored(e.g., by application 102 in FIG. 2) or may be received from remotesources (e.g., a bank or website). The transaction history data mayfactor in details of the transaction, such as amount of money, to whom,from whom, how many times, and/or success rate. Transaction/activitytypes may include, but are not limited to, loan/borrow funds or objects,buy from/sell to goods and services, financial transactions, dating,partner with (e.g., develop an alliance, start a new business with,invest with, etc.), becoming friends/acquaintances, rent to/from(including, e.g., renting cars, houses, hotel rooms, etc.), hire/workfor (including, e.g., plumber, babysitter, etc.). The activity ortransactions may include any number of parties, and each party may needto verify that they were in fact part of the activity/transaction. Eachparty may also rate their experience with the transaction/activity.Reminders for uncompleted activity/transactions may be automaticallysent to a user or entity. For example, an email may be sent askingwhether the user would like to provide feedback.

In some embodiments, the transactions history component 418 may compriseinteractions between previous transactions in the transaction historybetween a first entity and a second entity. In this manner, processingcircuitry may take into account elements of regret and forgiveness indetermining a trust score. For example, a first transaction maycorrespond to an increase or decrease in a trust score, while a second,subsequent transaction related to the first transaction may result in anadjustment to the peer trust score in the opposite direction. Theadjustment may be either a decrease in the trust score (e.g., regret orsuspicion) or an increase in the trust score (e.g., forgiveness orredemption). As an illustrative example, a subject may have stolen a carin the past and be subsequently convicted of the theft and sentenced toserve 3 years in prison for the crime. The initial theft may serve todecrease the subject's trust score, reflecting the increased suspicionassociated with a known delinquent, while the subsequent conviction andsentence might serve to increase the subject's trust score, reflecting alevel of redemption in the trustworthiness of the subject.

In some embodiments, the transactions that comprise the transactionshistory component 418 may be associated with an increase or decrease ina trust score over time. For example, a transaction may contribute to aninitial increase in a trust score, and over time, the initial increasemay decay until the trust score returns to an initial value. Similarly,a transaction may cause an initial decrease in a trust score, and overtime, the initial decrease may decay until the trust score returns to aninitial value.

In some embodiments, any one of the system, peer, or contextual trustscore may also include a location component that takes into account ageographic location of an entity. For example, the location of an enduser as determined by GPS coordinates or an address of a business may beincorporated into the calculation of a trust score. In some embodiments,a peer trust score may take into account the location of a first entityand a second entity and adjust the trust score accordingly. Forinstance, if a first user and a second user happen to be from the samehometown, then the peer trust scores may be increase to reflect thiscommon information. In some embodiments, the location of the entity mayprovide an automatic increase/decrease in the trust score. For instance,a particular location may be known as a dangerous neighborhood, city, orregion, and the trust scores of all entities located or associated withthe dangerous location may be automatically decreased to reflect thisdanger. As an illustrative example, a user who travels to a countryclose to a known warzone may not be as comfortable trusting strangers inthe country. The trust levels of others located in the same location asthe user may be automatically decreased to reflect the increasedsuspicion. In some embodiments, the user may be traveling with hisfriends, as indicated by the high level of peer trust scores the userhas with the plurality of people located around the user. Processingcircuitry may determine that the user is surrounded by friends in anysuitable manner, including explicit indications of friendship, commonhometown, place of work, or any other common information. If the user istraveling to a dangerous location, but is traveling with friends, thenthe trust scores of other entities associated with the dangerouslocation may still be decreased, but they may be decreased by a smalleramount than if the user was not traveling with friends.

In some embodiments, any of the system, peer, and/or contextual trustscores may take into account biological responses of an end user. Forinstance, mobile devices may include cell phones, smart watches, heartrate monitors, and other wearable mobile devices that can monitor one ormore biological responses of an end user (e.g., heart rate, breathingrate, brain waves, sweat response, etc.). These detected biologicalresponses of an end user, in conjunction with location information, maybe used, in part, to determine a trust score. For example, an increasein heart rate may be an indication of anxiety, and may result in adecrease in trust score. The increase in heart rate may be caused by theuser moving to a new location, in which case the trust score associatedwith that location may be decreased. The increase in heart rate may havebeen caused by a first user moving into close proximity with a seconduser, in which case the peer trust score with respect to the second usermay be decreased, to reflect the increased anxiety that the first userfeels around the second user.

In some embodiments, any of the system, peer, and/or contextual trustscores may take into account crowdsourced information. As discussedabove, crowdsourced information may refer to information provided aboutan entity from other entities (i.e., the “crowd). The crowd may provideany type of descriptive information about an entity, includingcharacteristics (e.g., Bob is financially responsible), features (e.g.,Bob's diner is a clean restaurant), relationships with others (e.g., Bobis my friend), user validation information (e.g., “That's me,” or“That's not me”), transaction history, indications of duplicate entriesfor entities, or any other type of descriptive information. Thiscrowdsourced information, including any of the above illustrativeexamples, are herein described as “attributes,” and may be associatedwith an entity and indicated on a profile of the entity.

An entity may be assigned an attribute in any suitable manner. Asdescribed above, an entity may be assigned an attribute by the crowd orby another entity. In some embodiments, the attribute may be assigned tothe entity by a system administrator. In some embodiments, the attributemay be automatically assigned to an entity based on any number offactors, including any of the component scores, any of the data used tocalculate the component scores, or the system, peer, or contextualscores. As an illustrative example, an entity with a system trust scoreabove a certain threshold value may automatically be assigned the“Trusted” attribute, which may provide multipliers to certain componentscores and/or the overall trust score.

A user interface may provide an opportunity for the crowd (i.e., otherentities) to provide feedback on one or more attributes. In someembodiments, the attribute may not receive feedback from the crowd. Forexample, entities may not be able to leave feedback on the “Trusted”attribute, which may be automatically assigned based on an entity'strust score. The user interface may provide user-selectable inputs thatallow the crowd to provide its feedback. For example, as discussedabove, the user interface may include a thumbs up/down icon,like/dislike icon, plus/minus icon, positive/negative icon, star-basedsystem, or a numeric-based system that allows the crowd to indicatewhether it agrees or disagrees with the attribute (and the magnitude ofits agreement/disagreement). In a binary feedback system, such as alike/dislike system, a net attribute score, as used herein, may refer tothe difference between a positive feedback and a negative feedback. In arating-based systems, such as a star-based or a numeric-based system,the net attribute score, as used herein, may refer to an average ratingprovided by the crowd. As discussed above, the net attribute score mayprovide an indication as to the degree to which the crowd agrees withthe attribute for the entity.

In some embodiments, the attributes associated with an entity may beintegrated into a trust score as one or more component scores. Forexample, the net attribute score or scores may comprise an individualcomponent score that is combined with other component scores asdescribed above in order to calculate a system, peer, or contextualtrust score.

In some embodiments, the attributes may relate to or correspond to oneof the component scores. In such embodiments, the component scores maybe adjusted based on the fact that an entity is associated with relatedattributes. For example, the fact that an entity is a “Trusted” entitymay increase one of the component scores and/or one of the system, peer,or contextual trust scores.

The component scores may be adjusted based on the attributes in anysuitable manner. In some embodiments, the attribute may cause acomponent score and/or one of the system, peer, or contextual trustscores, to increase by a predetermined amount. In some embodiments, theattribute may cause a multiplier to be applied to a component scoreand/or one of the system, peer, or contextual trust scores. In someembodiments, the adjustment may be limited by a maximum allowableadjustment or by a threshold component score. For example, theadjustment to any one component score may be limited to a certainpercentage (such as 10%) of the maximum component score. The componentscore itself may also have a threshold score that it cannot exceed. Forinstance, the court history component score may be limited to 100points, regardless of any adjustments that could be made based onattributes.

In some embodiments, the adjustment may be based on a net attributescore. For instance, a positive attribute may cause a related componentto increase as it receives more “likes” by the crowd. In someembodiments, the adjustment may be normalized based on the number oflikes received by other entities with the same attribute. For example,processing circuitry may identify a subset of entities of the crowd withthe same attribute and calculate an average and/or a distribution of thenet attribute score for the subset of entities. In some embodiments, theprocessing circuitry may estimate a Gaussian distribution for the netattribute scores of the subset of entities. By assuming the Gaussiandistribution, the processing circuitry may determine the percentile of anet attribute score of an entity. The percentile may determine themagnitude of the adjustment caused by the attribute. For example, of allof the entities with the attribute “student,” the average net attributescore may be 200 with a standard deviation of 100. If an entity has anet attribute score of 500, that may indicate that they are threestandard deviations higher than the average, or within the 0.1%percentile. The adjustment caused by such a high net attribute score,compared to other entities with the attribute “student” may berelatively high.

In some embodiments, the adjustments based on attributes and/or themaximum or threshold adjustment levels, may be determined by a systemadministrator. Such limits may prevent the attributes from affectingcomponent scores and/or trust scores greater than a predeterminedamount. In such embodiments, the component scores may be calculatedbased on relevant data received from data sources, as described above,and the attributes may only provide relatively small adjustments to thecomponent score. In some embodiments, data indicating such adjustments,such as tables or distributions, may be stored in a local or a remotedatabase.

FIG. 5 is a diagram 500 of a weighted combination 502 of components504-518 that comprise a trust score in accordance with certainembodiments of the present disclosure. It will be understood that atrust score may comprise more or fewer components than components504-518 and that components 504-518 are provided for illustrativepurposes only. Weighted combination 502 comprises a data verificationcomponent 504, a network connectivity component 506, a credit scorecomponent 508, a court data component 510, a ratings/feedback datacomponent 512, a group/demographics component 514, a search enginemining component 516, and a transaction history component 518. Thecomponents 504-518 may correspond respectively to data verificationcomponent 404, network connectivity component 406, credit scorecomponent 408, court data component 410, ratings/feedback data component412, group/demographics component 414, search engine mining component416, and transaction history component 418 depicted in FIG. 4. As shownin the illustrative example depicted in FIG. 5, the components 504-518may be combined using a default weighting according to the followingweights:

-   -   Data Verification—5%    -   Network Connectivity—20%    -   Credit Score—15%    -   Court Data—10%    -   Ratings/Feedback Data—20%    -   Group/Demographics—5%    -   Search Engine Mining—5%    -   Transaction History—20%        The components 504-518 may be combined using the above weights        using a weighted sum. For example, each of the component 504-518        may be associated with a numerical component score. The weighted        sum 502 may be calculated as:

$S = {\sum\limits_{i = 1}^{n}\; {w_{i}c_{i}}}$

wherein w_(i) is the weighting as given by the default weighting above,and c_(i) is the component score.

In some embodiments, the default weightings may be adjusted according touser-specified values. For example, as discussed above, users who caremore about network connectivity may increase the weighting for thenetwork connectivity component 506, and users who care less aboutfinancial responsibility may choose to decrease credit score component508. These default weightings may be saved for each specific entity andretrieved each time the user requests a trust score. In someembodiments, the default weightings above may be automatically adjusted,for example by application 102, to reflect a peer trust score orcontextual trust score. For example, application 102 may detect that afirst and second entity are entering into a financial transaction andmay automatically adjust the weight for the credit score component 508to reflect the importance of this component to the type of activity.These weightings may be saved for the individual entities and/or thespecific transaction types. Thus, the users may be provided with acontextual trust score that weights factors in a more relevant mannerthan the default weightings.

In some embodiments, at least one of the system trust score, peer trustscore, and contextual trust score may be represented by a mean value andconfidence band. The confidence band may represent a statisticalvariance in the calculated trust score. For example, each of thecomponent scores may be associated with a mean score μ and a standarddeviation σ based on how trustworthy the data source is. The mean andstandard deviation for each of the component scores may be combinedaccordingly. As will be understood by those of ordinary skill in theart, the mean value of the total component scores may be represented bya sum of the mean value of each component score. The variance of twocomponent scores together may be combined using the following equation:

V(A+B)=V(A)+V(B)+2*Covar(A,B)

where V(A) is the variance (i.e., the square of the standard deviation)of component A, V(B) is the variance of component B, and Covar(A,B) isthe covariance of components A and B.

FIG. 6 is a graphical user interface displaying a trust score interface600 to a requesting user in accordance with certain embodiments of thepresent disclosure. Trust score interface 600 includes icon 602, initialscore 604, transaction selector 606, transaction details field 608,additional transaction button 610, revised score icon 612, first profilescore 614, second profile score 616, and calculate button 618. Althoughthe trust score interface 600 is depicted in FIG. 6 in the context of amobile device display screen, it will be understood that trust scoreinterface 600 may be generated for display on any suitable displaydevice.

Icon 602 and initial score 604 may graphically represent a first trustscore of a target entity. Although icon 602 is depicted as a smileyface, it will be understood that any suitable graphical representationmay be utilized to represent a relative trust level of the targetentity. In some embodiments, the initial score 604 may be a system trustscore for the target entity calculated using a default set of weights.In other embodiments, the initial score 604 may be a peer trust scorecalculated in relation to the user of the mobile app. For instance, theinitial score 604 may represent a trust level that takes into accountmutual friends of the requesting user and the target user.

The requesting user may use transaction selector 606 to indicate anactivity/transaction to be performed with the target user. In someembodiments, transaction selector 606 may be optional, and notransaction is needed to calculate a revised score. Although transactionselector 606 is depicted as a dropdown box, any suitable input method(e.g., text input box, radio buttons, etc.) may be utilized to receivean indication of an activity/transaction from the requesting user. Afteran activity/transaction is selected, transaction details field 608 mayprovide further details or options. For example, if the requesting userindicates that the target entity wishes to request a loan, then thetransaction details field 608 may include a field for indicating theamount of the loan. In this manner, a different weighting of componentsmay be used for a $10 loan as opposed to a $100,000 loan. The requestinguser may add an additional transaction using additional transactionbutton 610. In cases where multiple transactions are indicated,weightings for the multiple transactions may be averaged.

Revised score icon 612 may indicate a revised trust score calculatedbased on the information entered into transaction selector 606 andtransaction details field 608. In some embodiments, the revised scoreicon 612 may reflect a peer trust score, for example, when a transactionis not selected in transaction selector 606. In other embodiments, therevised score icon 612 may reflect a contextual trust score calculatedbased on the activity/transaction and transaction details indicated intransaction selector 606 and transaction details field 608. The revisedscore icon 612 may include a graphical representation of the revisedtrust score, similar to icon 602. In the illustrative example depictedin FIG. 6, revised icon 612 includes a smiley face to represent arelatively high revised score of 673. The requesting user may request acalculation using calculation button 618.

The first profile score 614 and the second profile score 616 mayindicate one or more of a system trust score, peer trust score, and/orcontextual trust score for the requesting user. As with icon 602 andicon 612, the first profile score 614 and second profile score 616 mayinclude a graphical representation, such as a smiley face, of therespective trust score.

FIG. 7 is a graphical user interface displaying another trust scoreinterface 700 in accordance with certain embodiments of the presentdisclosure. Trust score interface 700 includes weighting profileselector 702, weighting details field 704, weighting selector 706, firstprofile score 708, second profile score 710, and update weighting button712.

As discussed above in relation to FIG. 5, a user may adjust weightingsto user-specified value. These user-specified weightings may be saved asprofiles which may be selected in weighting profile selector 702.Weighting details field 704 may reflect the details, such as weightingvalues of the various components, that correspond to the selectedweighting profile. A user may further adjust the weightings usingweighting selector 706. Although weighting profile selector 704 andweighting selector 706 are depicted in FIG. 7 as dropdown menus, anysuitable selector may be utilized, including, but not limited to, textinput boxes and/or radio buttons. The requesting user may update theweighting profile with the specified weights by selecting updateweighting button 712.

In some embodiments, the weighting profiles may be stored, for examplein data store 110 depicted in FIG. 1. These weighting profiles may formthe basis for developing default weighting profiles specific to aparticular transaction type. These default weighting profiles forspecific transaction types may be suggested to other users, and thesystem, using processing circuitry, may use AI/machine learningtechniques in order to monitor how users are adjusting the weightingprofiles and automatically readjust the default weighting profiles forother users. By doing so, the system may improve response time andconvenience for the end users, since they will not have to manuallyadjust their weighting profiles.

In some embodiments, the user may indicate an initial or base trustscore factor that may be applied to every other user. At least one ofthe system trust score, peer trust score, and contextual trust score maythen be calculated as updates to the initial or base trust score thatthe user has indicated. For example, each of the components discussed inrelation with FIG. 4 may result in an increase or decrease in theindicated initial or base trust score. In some embodiments, the initialor base trust score may be determined by presenting a questionnaire orseries of questions to the user to determine their general trust leveltowards other entities. In some embodiments the user may specifydifferent initial or base trust scores for different entities.

First profile score 708 and second profile score 710 may besubstantially similar to first profile score 614 and second profilescore 616 depicted in FIG. 6 and may indicate one or more of a systemtrust score, peer trust score, and/or contextual trust score for therequesting user.

FIG. 8 is a table 800 showing a graded scale for assigning componentscores based on a metric in accordance with certain embodiments of thepresent disclosure. Table 800 depicts but one illustrative example fordetermining a component score or subcomponent score based on a measuredmetric 802. The illustrative example depicted in FIG. 8 uses number offriends in a social network as a measurable metric. Based on metric 802,component scores 804 and 806 may be assigned according to a gradedscale. In the example depicted in FIG. 8, the component score 804 isdepicted as a numerical score out of 1000, and the component score 806is depicted as a percentage out of 100%. It will be understood that anysuitable method for depicting the component score may be used. Forexample, the component score may be a represented by discrete categories(e.g., “very bad,” “bad,” “ok,” “good,” and “very good”). Furthermore,although the graded scale depicted in FIG. 8 shows only five steps, thegraded scale may be divided into any suitable number of steps orcategories.

According to the graded scale depicted in FIG. 8, the network componentscore (e.g., network connectivity score 406 in FIG. 4) may be assignedbased on the number of friends the target entity has. For example, ifthe target entity has 306 friends, the network component score may be600. In some embodiments, the network component score may comprise acombination of two or more subcomponent scores, wherein eachsubcomponent score is determined based on a grade scale similar to table800. In some embodiments, the subcomponent scores may also be determinedbased on the method discussed below in relation to FIG. 9. In someembodiments, the subcomponent scores may be combined using, for example,an average or a weighted average or other suitable combination function.For example, the network component score may combine the number offriends and the number of “likes” a target user has received on theirposts. The network component score may be weighted so that the number offriends accounts for 700/1000 of the potential network component score,and the number of “likes” accounts for 300/1000 of the potential networkcomponent score.

The metric 802 and the steps of the graded scale may be determined by aserver, such as application server 106 depicted in FIG. 1. For example,the provider of the trust app may set the metric according to theirproprietary algorithm. In some embodiments, the metric 802 may beadjusted by an entity such that the component score may be calculatedaccording to the user's preferences. Although the metric 802 isdiscussed with respect to a network connectivity score, it will beunderstood that any of the components 404-418, or any other components,may be determined using a similar graded scale scheme.

FIG. 9 is a distribution 900 for assigning component scores based on ametric in accordance with certain embodiments of the present disclosure.Distribution 900 depicts one illustrative example for determining acomponent score or subcomponent score based on a measured metric 902.The illustrative example depicted in FIG. 9 uses number of friends in asocial network as a measurable metric 904. An application (such asaccess application 102 in FIG. 1) or an application server (such asapplication server 106 in FIG. 1) may identify entities connected to arequesting user through a network. In some embodiments, the network maybe a social network (such as Facebook) or a computer network (such asthe Internet or a subset of the Internet). The application orapplication server may then determine or retrieve, for each identifieduser, information on the desired metric 904. In the illustrative exampledepicted in FIG. 9, the application or application server may identifyall of the requesting user's friends and determine how many friends eachof the user's friends has. Distribution 900 may be graphed based on thedetermined or retrieved information. In FIG. 9, distribution 900 isdepicted as a Gaussian distribution, but it will be understood that anydistribution may result from the determined or retrieved data. Thedistribution 900 may have a peak 912 at an average value μ. Forinstance, most of a requesting user's friends may have an average valueof μ=500 friends. The distribution 900 may be divided into regions 906,908, 910, 914, 916, and 918 based on a standard deviation σ. Forexample, region 906 may represent a number of friends that is twostandard deviations σ below the average value μ. Region 908 mayrepresent a number of friends that is between two standard deviations σand one standard deviation σ below the average value μ. Region 910 mayrepresent a number of friends that is less than one standard deviation σbelow the average value μ. Region 914 may represent a number of friendsthat is between the average value μ and one standard deviation σ abovethe average value μ. Region 916 may represent a number of friends thatis between one standard deviation σ and two standard deviations σ abovethe average value μ. Finally, region 918 may represent a number offriends that is above two standard deviations σ above the average valueμ.

The metric for the target user may fall into one of regions 906, 908,910, 914, 916, and 918. As will be understood by those of ordinary skillin the art, regions 906 and 918 represent about 2.5% each ofdistribution 900, regions 908 and 916 represent about 13.5% each ofdistribution 900, and regions 910 and 914 represent about 34% each ofdistribution 900. The application or application server may assign acomponent score depending on which of regions 906, 908, 910, 914, 916,and 918 the metric of the target user falls into. For instance, thecomponent score for the target user may be relatively low if the metricfalls within regions 906 or 918 and may be relatively high if the metricfalls within regions 910 or 914. A graded scale, similar to table 800depicted in FIG. 8, may be assigned to the regions 906, 908, 910, 914,916, and 918.

FIG. 10 is a display of a network graph 1000 in accordance with certainembodiments of the present disclosure. Network graph 1000 includessource node 1002, target node 1004, intermediate node 1006, and paths1008 and 1010. The network graph 1000 may be generated for display onany suitable display device and in any suitable interface, such as theinterfaces 600 and 700 depicted in FIGS. 6 and 7. As defined herein, a“node” may include any user terminal, network device, computer, mobiledevice, access point, or any other electronic device. In someembodiments, a node may also represent an individual human being, entity(e.g., a legal entity, such as a public or private company, corporation,limited liability company (LLC), partnership, sole proprietorship, orcharitable organization), concept (e.g., a social networking group),animal, or inanimate object (e.g., a car, aircraft, or tool).

The network graph 1000 may represent a visualization of a network thatconnects a requesting entity, depicted by source node 1002, and a targetentity, depicted by target node 1004. One or more intermediate nodes,such as intermediate node 1006, may also be displayed, as well as paths1008 that connect nodes 1002, 1004, and 1006. In some embodiments, adominant path 1010 may be displayed and visually distinguished fromother paths 1008. The dominant path 1010 may be determined using anysuitable algorithm. For example, the dominant path 1010 may representthe shortest-length path from source node 1002 to source node 1004. Inother embodiments, the dominant path 1010 may represent a path throughspecific intermediate nodes, such as nodes with relatively high trustvalues. For example, a longer path from node 1002 through node 1006 tonode 1004 may have higher trust at each link of the path than theshorter path 1010.

In some embodiments, each of the nodes 1002, 1004, and 1006 may includeimages, text, or both, such as a profile picture associated with theentity depicted by the nodes. In some embodiments, the network graph1000 may be generated for display in a scrollable display, wherein auser may scroll and zoom the network graph 1000 to see more and lessnodes as desired.

FIGS. 11-13 describe illustrative methods for calculating a networkcomponent score, such as network connectivity component 406 depicted inFIG. 4. Connectivity may be determined, at least in part, using variousgraph traversal and normalization techniques described in more detailbelow.

In an embodiment, a path counting approach may be used where processingcircuitry is configured to count the number of paths between a firstnode n₁ and a second node n₂ within a network community. A connectivityrating R_(n1n2) may then be assigned to the nodes. The assignedconnectivity rating may be proportional to the number of subpaths, orrelationships, connecting the two nodes, among other possible measures.Using the number of subpaths as a measure, a path with one or moreintermediate nodes between the first node n₁ and the second node n₂ maybe scaled by an appropriate number (e.g., the number of intermediatenodes) and this scaled number may be used to calculate the connectivityrating.

In some embodiments, weighted links are used in addition to or as analternative to the subpath counting approach. Processing circuitry maybe configured to assign a relative user weight to each path connecting afirst node n₁ and a second node n₂ within a network community. A userconnectivity value may be assigned to each link. For example, a user orentity associated with node n₁ may assign user connectivity values forall outgoing paths from node n₁. In some embodiments, the connectivityvalues assigned by the user or entity may be indicative of that user orentity's trust in the user or entity associated with node n₂. The linkvalues assigned by a particular user or entity may then be compared toeach other to determine a relative user weight for each link.

The relative user weight for each link may be determined by firstcomputing the average of all the user connectivity values assigned bythat user (i.e., the out-link values). If t_(i) is the user connectivityvalue assigned to link i, then the relative user weight, w_(i), assignedto that link may be given in accordance with:

w _(i)=1+(t _(i)− t _(i) )²   (1)

To determine the overall weight of a path, in some embodiments, theweights of all the links along the path may be multiplied together. Theoverall path weight may then be given in accordance with:

w _(path)=Π(w _(i))   (2)

The connectivity value for the path may then be defined as the minimumuser connectivity value of all the links in the path multiplied by theoverall path weight in accordance with:

t _(path) =w _(path) ×t _(min)   (3)

To determine path connectivity values, in some embodiments, a parallelcomputational framework or distributed computational framework (or both)may be used. For example, in one embodiment, a number of core processorsimplement an Apache Hadoop or Google MapReduce cluster. This cluster mayperform some or all of the distributed computations in connection withdetermining new path link values and path weights.

The processing circuitry may identify a changed node within a networkcommunity. For example, a new outgoing link may be added, a link may beremoved, or a user connectivity value may have been changed. In responseto identifying a changed node, in some embodiments, the processingcircuitry may re-compute link, path, and weight values associated withsome or all nodes in the implicated network community or communities.

In some embodiments, only values associated with affected nodes in thenetwork community are recomputed after a changed node is identified. Ifthere exists at least one changed node in the network community, thechanged node or nodes may first undergo a prepare process. The prepareprocess may include a “map” phase and “reduce” phase. In the map phaseof the prepare process, the prepare process may be divided into smallersub-processes which are then distributed to a core in the parallelcomputational framework cluster. For example, each node or link change(e.g., tail to out-link change and head to in-link change) may be mappedto a different core for parallel computation. In the reduce phase of theprepare process, each out-link's weight may be determined in accordancewith equation (1). Each of the out-link weights may then be normalizedby the sum of the out-link weights (or any other suitable value). Thenode table may then be updated for each changed node, its in-links, andits out-links.

After the changed nodes have been prepared, the paths originating fromeach changed node may be calculated. Once again, a “map” and “reduce”phase of this process may be defined. During this process, in someembodiments, a depth-first search may be performed of the node digraphor node tree. All affected ancestor nodes may then be identified andtheir paths recalculated.

In some embodiments, to improve performance, paths may be grouped by thelast node in the path. For example, all paths ending with node n₁ may begrouped together, all paths ending with node n₂ may be grouped together,and so on. These path groups may then be stored separately (e.g., indifferent columns of a single database table). In some embodiments, thepath groups may be stored in columns of a key-value store implementingan HBase cluster (or any other compressed, high performance databasesystem, such as BigTable).

In some embodiments, one or more threshold functions may be defined. Thethreshold function or functions may be used to determine the maximumnumber of links in a path that will be analyzed in a connectivitydetermination or connectivity computation. Threshold factors may also bedefined for minimum link weights, path weights, or both. Weights fallingbelow a user-defined or system-defined threshold may be ignored in aconnectivity determination or connectivity computation, while onlyweights of sufficient magnitude may be considered.

In some embodiments, a user connectivity value may represent the degreeof trust between a first node and a second node. In one embodiment, noden₁ may assign a user connectivity value of l₁ to a link between it andnode n₂. Node n₂ may also assign a user connectivity value of l₂ to areverse link between it and node n₁. The values of l₁ and l₂ may be atleast partially subjective indications of the trustworthiness of theindividual or entity associated with the node connected by the link. Auser (or other individual authorized by the node) may then assign thisvalue to an outgoing link connecting the node to the individual orentity. Objective measures (e.g., data from third-party ratings agenciesor credit bureaus) may also be used, in some embodiments, to formcomposite user connectivity values indicative of trust. The subjective,objective, or both types of measures may be automatically harvested ormanually inputted for analysis.

FIG. 11 shows data tables 1100 used to support the connectivitydeterminations for calculating a network component score in accordancewith certain embodiments of the present disclosure. One or more oftables 1100 may be stored in, for example, a relational database in datastore 110 (FIG. 1). Table 1102 may store an identification of all thenodes registered in a network community. A unique identifier may beassigned to each node and stored in table 1102. In addition, a stringname may be associated with each node and stored in table 1102. Asdescribed above, in some embodiments, nodes may represent individuals orentities, in which case the string name may include the individual orperson's first and/or last name, nickname, handle, or entity name.

Table 1104 may store user connectivity values. In some embodiments, userconnectivity values may be assigned automatically by the system (e.g.,by application server 106 (FIG. 1)). For example, application server 106(FIG. 1) may monitor all electronic interaction (e.g., electroniccommunication, electronic transactions, or both) between members of anetwork community. In some embodiments, a default user connectivityvalue (e.g., the link value 1) may be assigned initially to all links inthe network community. After electronic interaction is identifiedbetween two or more nodes in the network community, user connectivityvalues may be adjusted upwards or downwards depending on the type ofinteraction between the nodes and the result of the interaction. Forexample, each simple email exchange between two nodes may automaticallyincrease or decrease the user connectivity values connecting those twonodes by a fixed amount. More complicated interactions (e.g., product orservice sales or inquiries) between two nodes may increase or decreasethe user connectivity values connecting those two nodes by some largerfixed amount. In some embodiments, user connectivity values between twonodes may be increased unless a user or node indicates that theinteraction was unfavorable, not successfully completed, or otherwiseadverse. For example, a transaction may not have been timely executed oran email exchange may have been particularly displeasing. Adverseinteractions may automatically decrease user connectivity values whileall other interactions may increase user connectivity values (or have noeffect). In addition, user connectivity values may be automaticallyharvested using outside sources. For example, third-party data sources(such as ratings agencies and credit bureaus) may be automaticallyqueried for connectivity information. This connectivity information mayinclude completely objective information, completely subjectiveinformation, composite information that is partially objective andpartially subjective, any other suitable connectivity information, orany combination of the foregoing.

In some embodiments, user connectivity values may be manually assignedby members of the network community. These values may represent, forexample, the degree or level of trust between two users or nodes or onenode's assessment of another node's competence in some endeavor. Userconnectivity values may include a subjective component and an objectivecomponent in some embodiments. The subjective component may include atrustworthiness “score” indicative of how trustworthy a first user ornode finds a second user, node, community, or subcommunity. This scoreor value may be entirely subjective and based on interactions betweenthe two users, nodes, or communities. This manual user connectivityscore may “override” one or more of the system trust score, peer trustscore, or contextual trust score. When a user “overrides” one of theabove trust scores with a manual trust score, the user-specified trustscore may be provided concurrently with, or instead of, the overriddentrust score.

In some embodiments, a system administrator may override one or more ofthe system trust score, peer trust score, or contextual trust score. Forexample, a system administrator may override a system trust score of anentity to take into account recent trends or events. When a trust scoreis overridden by the system administrator, the administrator's trustscore may be provided concurrently with, or instead of, the overriddentrust score. When the overridden trust score reaches a specified rangeor threshold of the administrator's trust score, the system mayautomatically revert back to the overridden trust score. As anillustrative example, the system administrator may decrease a systemtrust score of an entity that has taken negative public attention in thenews. The overridden trust score will continue to be calculated by thesystem and will gradually reflect the negative public attention of theentity. When the overridden trust score reaches within a certain rangeof the administrator's trust level (e.g., within 10%), then the systemwill automatically revert back to the calculated score. In someembodiments, the administrator's trust score will be provided to a userwith a notification that the score was overridden and/or a reason whythe trust score was overridden.

Table 1104 may store an identification of a link head, link tail, anduser connectivity value for the link. Links may or may not bebidirectional. For example, a user connectivity value from node n₁ tonode n₂ may be different (and completely separate) than a link from noden₂ to node n₁. Especially in the trust context described above, eachuser can assign his or her own user connectivity value to a link (i.e.,two users need not trust each other an equal amount in someembodiments).

Table 1106 may store an audit log of table 1104. Table 1106 may beanalyzed to determine which nodes or links have changed in the networkcommunity. In some embodiments, a database trigger is used toautomatically insert an audit record into table 1106 whenever a changeof the data in table 1104 is detected. For example, a new link may becreated, a link may be removed, or a user connectivity value may bechanged. This audit log may allow for decisions related to connectivityvalues to be made prospectively (i.e., before an anticipated event).Such decisions may be made at the request of a user, or as part of anautomated process. This prospective analysis may allow for theinitiation of a transaction (or taking of some particular action) in afluid and/or dynamic manner. After such a change is detected, thetrigger may automatically create a new row in table 1106. Table 1106 maystore an identification of the changed node, and identification of thechanged link head, changed link tail, and the user connectivity value tobe assigned to the changed link. Table 1106 may also store a timestampindicative of the time of the change and an operation code. In someembodiments, operation codes may include “insert,” “update,” or “delete”operations, corresponding to whether a link was inserted, a userconnectivity value was changed, or a link was deleted, respectively.Other operation codes may be used in other embodiments.

FIG. 12 shows data structure 1210 used to support the connectivitydeterminations of the present disclosure. In some embodiments, datastructure 1210 may be stored using key-value store 112 (FIG. 1), whiletables 1200 are stored in data store 110 (FIG. 1). As described above,key-value store 112 (FIG. 1) may implement an HBase storage system andinclude BigTable support. Like a traditional relational databasemanagement system, the data shown in FIG. 12 may be stored in tables.However, the BigTable support may allow for an arbitrary number ofcolumns in each table, whereas traditional relational databasemanagement systems may require a fixed number of columns.

Data structure 1210 may include node table 1212. In the example shown inFIG. 12, node table 1212 includes several columns. Node table 1212 mayinclude row identifier column 1214, which may store 64-bit, 128-bit,256-bit, 512-bit, or 1024-bit integers and may be used to uniquelyidentify each row (e.g., each node) in node table 1212. Column 1216 mayinclude a list of all the incoming links for the current node. Column1218 may include a list of all the outgoing links for the current node.Column 1220 may include a list of node identifiers to which the currentnode is connected. A first node may be connected to a second node ifoutgoing links may be followed to reach the second node. For example,for A→B, A is connected to B, but B may not be connected to A. Nodetable 1212 may also include one or more “bucket” columns 1222. Thesecolumns may store a list of paths that connect the current node to atarget node. As described above, grouping paths by the last node in thepath (e.g., the target node) may facilitate connectivity computations.As shown in FIG. 12, in some embodiments, to facilitate scanning, bucketcolumn names may include the target node identifier appended to the endof the “bucket:” column.

FIGS. 13A-13E show illustrative processes for determining theconnectivity of nodes within a network community. The processes depictedin FIGS. 13A-13E may be used to determine a network component score,such as network connectivity component 406 depicted in FIG. 4. FIG. 13Ashows process 1300 for updating a connectivity graph (or any othersuitable data structure) associated with a network community. Asdescribed above, in some embodiments, each network community isassociated with its own connectivity graph, digraph, tree, or othersuitable data structure. In other embodiments, a plurality of networkcommunities may share one or more connectivity graphs (or other datastructure).

In some embodiments, the processes described with respect to FIGS.13A-13E may be executed to make decisions prospectively (i.e., before ananticipated event). Such decisions may be made at the request of a user,or as part of an automated process. This prospective analysis may allowfor the initiation of a transaction (or taking of some particularaction) in a fluid and/or dynamic manner. In some embodiments,processing circuitry may anticipate an increase or decrease in a trustscore as a result of making a certain decision. The processing circuitrymay provide an alert to an end user, for example through one of userinterface 600 or 700, that indicates to the end user that the trustscore of the end user will increase/decrease as a result of thedecision. In some embodiments, the prospective decision may also bemade, either manually or automatically, based on the potentialincrease/decrease in trust score as a result of the decision. Forexample, processing circuitry may automatically make a prospectivedecision if the decision would result in an increase/decrease in a trustscore within a certain threshold. In this manner, prospective decisions,whether made automatically or manually, may take into account a risktolerance or risk preference of an end user.

At step 1302, a determination is made whether at least one node haschanged in the network community. As described above, an audit recordmay be inserted into table 1106 (FIG. 11) after a node has changed. Byanalyzing table 1106 (FIG. 11), a determination may be made (e.g., byapplication server 106 of FIG. 1) that a new link has been added, anexisting link has been removed, or a user connectivity value haschanged. If, at step 1304, it is determined that a node has changed,then process 1300 continues to step 1310 (shown in FIG. 13B) to preparethe changed nodes, step 1312 (shown in FIG. 13C) to calculate pathsoriginating from the changed nodes, step 1314 (shown in FIG. 13D) toremove paths that go through a changed node, and step 1316 (shown inFIG. 13E) to calculate paths that go through a changed node. It shouldbe noted that more than one step or task shown in FIGS. 13B, 13C, 13D,and 13E may be performed in parallel using, for example, a cluster ofcores. For example, multiple steps or tasks shown in FIG. 13B may beexecuted in parallel or in a distributed fashion, then multiple steps ortasks shown in FIG. 13C may be executed in parallel or in a distributedfashion, then multiple steps or tasks shown in FIG. 13D may be executedin parallel or in a distributed fashion, and then multiple steps ortasks shown in FIG. 13E may be executed in parallel or in a distributedfashion. In this way, overall latency associated with process 1300 maybe reduced.

If a node change is not detected at step 1304, then process 1300 entersa sleep mode at step 1306. For example, in some embodiments, anapplication thread or process may continuously check to determine if atleast one node or link has changed in the network community. In otherembodiments, the application thread or process may periodically checkfor changed links and nodes every n seconds, where n is any positivenumber. After the paths are calculated that go through a changed node atstep 1316 or after a period of sleep at step 1306, process 1300 maydetermine whether or not to loop at step 1308. For example, if allchanged nodes have been updated, then process 1300 may stop at step1318. If, however, there are more changed nodes or links to process,then process 1300 may loop at step 1308 and return to step 1304.

In practice, one or more steps shown in process 1300 may be combinedwith other steps, performed in any suitable order, performed in parallel(e.g., simultaneously or substantially simultaneously), or removed.

FIGS. 13B-13E each include processes with a “map” phase and “reduce”phase. As described above, these phases may form part of a map/reducecomputational paradigm carried out by parallel computational framework114 (FIG. 1), key-value store 112 (FIG. 1), or both. As shown in FIG.13B, in order to prepare any changed nodes, map phase 1320 may includedetermining if there are any more link changes at step 1322, retrievingthe next link change at step 1340, mapping the tail to out-link changeat step 1342, and mapping the head to in-link change at step 1344.

If there are no more link changes at step 1322, then, in reduce phase1324, a determination may be made at step 1326 that there are more nodesand link changes to process. If so, then the next node and its linkchanges may be retrieved at step 1328. The most recent link changes maybe preserved at step 1330 while any intermediate link changes arereplaced by more recent changes. For example, the timestamp stored intable 1106 (FIG. 11) may be used to determine the time of every link ornode change. At step 1332, the average out-link user connectivity valuemay be calculated. For example, if node n₁ has eight out-links withassigned user connectivity values, these eight user connectivity valuesmay be averaged at step 1332. At step 1334, each out-link's weight maybe calculated in accordance with equation (1) above. All the out-linkweights may then be summed and used to normalize each out-link weight atstep 1336. For example, each out-link weight may be divided by the sumof all out-link weights. This may yield a weight between 0 and 1 foreach out-link. At step 1338, the existing buckets for the changed node,in-links, and out-links may be saved. For example, the buckets may besaved in key-value store 112 (FIG. 1) or data store 110 (FIG. 1). Ifthere are no more nodes and link changes to process at step 1326, theprocess may stop at step 1346.

As shown in FIG. 13C, in order to calculate paths originating fromchanged nodes, map phase 1348 may include determining if there are anymore changed nodes at step 1350, retrieving the next changed node atstep 1366, marking existing buckets for deletion by mapping changednodes to the NULL path at step 1368, recursively generating paths byfollowing out-links at step 1370, and if the path is a qualified path,mapping the tail to the path. Qualified paths may include paths thatsatisfy one or more predefined threshold functions. For example, athreshold function may specify a minimum path weight. Paths with pathweights greater than the minimum path weight may be designated asqualified paths.

If there are no more changed nodes at step 1350, then, in reduce phase1352, a determination may be made at step 1354 that there are more nodesand paths to process. If so, then the next node and its paths may beretrieved at step 1356. At step 1358, buckets may be created by groupingpaths by their head. If a bucket contains only the NULL path at step1360, then the corresponding cell in the node table may be deleted atstep 1362. If the bucket contains more than the NULL path, then at step1364 the bucket is saved to the corresponding cell in the node table. Ifthere are no more nodes and paths to process at step 1356, the processmay stop at step 1374.

As shown in FIG. 13D, in order to remove paths that go through a changednode, map phase 1376 may include determining if there are any morechanged nodes at step 1378 and retrieving the next changed node at step1388. At step 1390, the “bucket:” column in the node table (e.g., column1222 of node table 1212 (both of FIG. 12)) corresponding to the changednode may be scanned. For example, as described above, the target nodeidentifier may be appended to the end of the “bucket:” column name. Eachbucket may include a list of paths that connect the current node to thetarget node (e.g., the changed node). At step 1392, for each matchingnode found by the scan and the changed node's old buckets, the matchingnode may be matched to a (changed node, old bucket) deletion pair.

If there are no more changed nodes at step 1378, then, in reduce phase1380, a determination may be made at step 1384 that there are more nodeand deletion pairs to process. If so, then the next node and itsdeletion pairs may be retrieved at step 1384. At step 1386, for eachdeletion pair, any paths that go through the changed node in the oldbucket may be deleted. If there are no more nodes and deletion pairs toprocess at step 1382, the process may stop at step 1394.

As shown in FIG. 13E, in order to calculate paths that go through achanged node, map phase 1396 may include determining if there are anymore changed nodes at step 1398 and retrieving the next changed node atstep 1408. At step 1410, the “bucket:” column in the node table (e.g.,column 1222 of node table 1212 (both of FIG. 12)) corresponding to thechanged node may be scanned. At step 1412, for each matching node foundin the scan and the changed node's paths, all paths in the scannedbucket may be joined with all paths of the changed bucket. At step 1414,each matching node may be mapped to each qualified joined path.

If there are no more changed nodes at step 1398, then, in reduce phase1400, a determination may be made at step 1402 that there are more nodeand paths to process. If so, then the next node and its paths may beretrieved at step 1404. Each path may then be added to the appropriatenode bucket at step 1406. If there are no more nodes and paths toprocess at step 1402, the process may stop at step 1416.

FIG. 14 shows a process 1420 for calculating a system trust score inaccordance with certain embodiments of the present disclosure. Process1420 includes verifying at least one entry in the entity's profile atstep 1422, determining connectivity metrics for a social network at step1424, performing a web search to determine publicly availableinformation at step 1426, identifying past transactions at step 1428,receiving ratings information from a third-party source at step 1430,calculating component scores at step 1432, determining whether userweightings have been received at step 143, combining component scoresusing default weights at step 1436, and combining component scores usinguser weights at step 1438. It will be understood that process 1420depicts illustrative steps for calculating a system trust score, andthat one or more of steps 1422-1438 may be omitted and additional stepsadded to process 1420 as will be apparent to those of skill in the artwithout departing from the scope hereof.

At step 1422, processing circuitry, such as processing circuitry ofaccess application 102 or application server 106, may verify at leastone entry in an entity's profile. The entry may be one or more pieces ofverification data, such as verification data described in connectionwith data verification component 404 depicted in FIG. 4. For example,the processing circuitry may verify one or more of a human user's emailaddress, phone number, mailing address, education information,employment information. At step 1424, the processing circuitry maydetermine connectivity metrics for a social network. The connectivitymetrics may comprise metrics as discussed in connection with networkconnectivity component 406 depicted in FIG. 4. The connectivity metricsmay include, but are not limited to, number of friends, number of posts,or number of messages. At step 1426, the processing circuitry mayperform a web search to determine publicly available informationassociated with the entity. For example, the processing circuitry mayperform search engine mining as discussed above in relation to searchengine mining component 416 depicted in FIG. 4. The processing circuitrymay also determine information such as the entity's credit score oravailable court data, as discussed above in relation to credit scorecomponent 408 and court data component 410 depicted in FIG. 4. At step1428, the processing circuitry may identify past transactions associatedwith the entity. For example, the processing circuitry may identify pastfinancial transactions that the entity has taken part in and whether thefinancial transactions were completed favorably (e.g., paid back a loan)or unfavorably (e.g., defaulted on a loan). At step 1430, the processingcircuitry may receive ratings information from a third-party source, asdiscussed above in relation to ratings/feedback data component 412depicted in FIG. 4. As an illustrative example, the processing circuitrymay receive ratings from the Better Business Bureau or from an onlineratings site such as Yelp about an entity. At 1432, the processingcircuitry may calculate component scores based on the informationreceived from steps 1424-1430. The processing circuitry may calculatethe components scores in any suitable manner, such as the methodsdiscussed above in FIGS. 8 and 9.

At step 1434, the processing circuitry may determine whetheruser-specified weightings have been received. For example, a user mayhave specified custom weightings through a user interface such asinterface 700 depicted in FIG. 7. If user-specified weightings have beenreceived, then the processing circuitry may combine the component scoresusing the user-specified weights at step 1438. If user-specified weightshave not been received, then the processing circuitry may combine thecomponent scores using default weights at step 1436, such as the defaultweights depicted in FIG. 5. In some embodiments, the processingcircuitry may calculate the system trust score in response to a userrequest for the system trust score. For example, the user may presscalculate button 618 depicted in FIG. 6, and in response, the processingcircuitry may calculate the system trust score in substantiallyreal-time. In other embodiments, the processing circuitry may calculatethe system trust score in advance of a user request for the system trustscore. In such embodiments, the processing circuitry may retrieve apre-calculated system trust score, for example from data store 110depicted in FIG. 1, in response to the user request for the system trustscore.

FIG. 15 shows a process 1500 for calculating a peer trust score inaccordance with certain embodiments of the present disclosure. Process1500 includes receiving a system trust score at step 1502, identifyingpaths from a first entity to a second entity at step 1504, receivingdata from a remote source associated with at least one of the firstentity or the second entity at step 1506, updating component scores atstep 1508, and calculating a peer trust score based on the updatedcomponent scores at step 1510. It will be understood that process 1500depicts illustrative steps for calculating a peer trust score, and thatone or more of steps 1502-1510 may be omitted and additional steps addedto process 1500 as will be apparent to those of skill in the art withoutdeparting from the scope hereof. For example, the process 1500 forcalculating a peer trust score is depicted in FIG. 15 as an update to asystem trust score. However, it will be understood that the peer trustscore may be calculated from component scores independently from asystem trust score, as discussed above.

At step 1502, processing circuitry, such as processing circuitry ofaccess application 102 or application server 106, may receive a systemtrust score. The system trust score may have been calculated previously,such as by a method similar to process 1420 depicted in FIG. 14. At step1504, the processing circuitry may identify paths from a first entity toa second entity. For example, the processing circuitry may utilize apath counting approach, as discussed above in relation to FIGS. 11-13.At step 1506, the processing circuitry my receive data from a remotesource associated with at least one of the first entity or the secondentity. For example, the processing circuitry may receive data regardingthe second entity's social connections, credit score, court data, orprevious transaction history with the first entity.

At step 1508, the processing circuitry may update component scores basedon the information from steps 1502-1506. In some embodiments, updatingcomponent scores comprises updating less than all of the componentscores that comprise the system trust score. For example, the processingcircuitry may only update the network connectivity component to takeinto account the mutual contacts of the first entity and the secondentity. Other component scores that were calculated with respect to thesecond entity's system trust score, such as credit score or court data,may not be affected by the additional social graph information. At step1510, the processing circuitry may calculate the peer trust score basedon the updated components by, for instance, combining the componentscores using a weighted combination, such as a weighted average or othersuitable weighting method. In some embodiments, the processing circuitrymay calculate the peer trust score in response to a user request for thepeer trust score. For example, the user may press calculate button 618depicted in FIG. 6, and in response, the processing circuitry maycalculate the peer trust score in substantially real-time. In otherembodiments, the processing circuitry may calculate the peer trust scorein advance of a user request for the peer trust score. In suchembodiments, the processing circuitry may retrieve a pre-calculated peertrust score, for example from data store 110 depicted in FIG. 1, inresponse to the user request for the peer trust score.

FIG. 16 shows a process 1600 for calculating a contextual trust score inaccordance with certain embodiments of the present disclosure. Process1600 includes receiving a peer trust score at step 1602, receiving anindication of an activity to be performed by a first entity and a secondentity at step 1604, updating component scores based on the activity atstep 1606, updating weights based on the activity at step 1608, andcalculating a contextual score based on the updated component scores andthe updated weights at step 1610. It will be understood that process1600 depicts illustrative steps for calculating a contextual trustscore, and that one or more of steps 1602-1610 may be omitted andadditional steps added to process 1600 as will be apparent to those ofskill in the art without departing from the scope hereof. For example,the process 1600 for calculating a peer trust score is depicted in FIG.16 as an update to a peer trust score. However, it will be understoodthat the contextual trust score may be calculated from component scoresindependently from a system trust score or a peer trust score, asdiscussed above.

At step 1602, processing circuitry, such as processing circuitry ofaccess application 102 or application server 106, may receive a peertrust score. The system trust score may have been calculated previously,such as by a method similar to process 1500 depicted in FIG. 15. At step1604, the processing circuitry may receive an indication of an activityto be performed by a first entity and a second entity. For example, theprocessing circuitry may receive the indication of the activity throughtransaction selector 606 depicted in FIG. 6. The processing circuitrymay also receive details of the activity/transaction through transactiondetails field 608, as discussed above in relation to FIG. 6. At step1606, the processing circuitry may update component scores based on theactivity. For example, certain component scores may be affected by atype of transaction. As an illustrative example, the transaction historycomponent, such as transaction history component 418 depicted in FIG. 4,may be updated to reflect only the transaction history of the particulartype of transaction that is being performed by the first and secondentity. At step 1608, the processing circuitry may update weights basedon the activity. As discussed above in relation to FIG. 7, differenttransaction types may be associated with different weightings, and thecomponents may be combined according to these different weightings. Atstep 1610, the processing circuitry may calculate the contextual trustscore based on the updated component scores and the updated weights, forexample, by taking a weighted combination, such as a weighted average,of the updated component scores according to the updated weights. Insome embodiments, the processing circuitry may calculate the contextualtrust score in response to a user request for the contextual trustscore. For example, the user may press calculate button 618 depicted inFIG. 6, and in response, the processing circuitry may calculate thecontextual trust score in substantially real-time. In other embodiments,the processing circuitry may calculate the contextual trust score inadvance of a user request for the contextual trust score. In suchembodiments, the processing circuitry may retrieve a pre-calculatedcontextual trust score, for example from data store 110 depicted in FIG.1, in response to the user request for the contextual trust score.

FIG. 17 is an illustrative process 1700 for adjusting weighting profilesbased on user inputs. Process 1700 includes transmitting a weightingprofile to a plurality of user devices at 1702, receiving inputs fromthe user devices adjusting the weighting profile at 1704, determiningwhether the inputs are within a threshold difference of the weightingprofile at 1706, updating the weighting profile based on the receivedinputs at 1708, and transmitting the updated weighting profile to atleast one of the plurality of user devices at 1710. It will beunderstood that process 1700 depicts illustrative steps for adjustingweighting profiles based on user inputs, and that one or more of steps1702-1710 may be omitted and additional steps added to process 1700 aswill be apparent to those of skill in the art without departing from thescope hereof.

At 1702, processing circuitry may transmit a weighting profile to aplurality of user devices. The weighting profile may be a defaultweighting profile comprising a set of weights for calculating a trustscore. Each weight of the set of weights may correspond to data from adata source, and the set of weights may be used to calculate, forexample, a weighted average for combining the data from the various datasources. At 1704, the processing circuitry may receive inputs from theuser devices adjusting the weighting profile. For instance, entities mayadjust the weights in the weighting profile using a user interfacesimilar to the interface depicted in FIG. 7. In some embodiments,adjustment of one weight in the set of weights may require acorresponding adjustment, either automatically or manually by theentity, of one or more other weights in the set of weights. As anillustrative example, an increase of 10% of one component may requirethe user to reduce other weights by a collective 10% (for instance byreducing one other component by 10% or five other components by 2%each).

At 1706, the processing circuitry may optionally determine whether theinputs are within a threshold difference of the weighting profile. Ifthe inputs are not within a threshold difference, then the processingcircuitry may return to 1704. For example, large changes to weights maybe ignored by the processing circuitry as outliers when updating adefault weighting profile. At 1708, if the inputs are within a thresholddifference of the weighting profile, the processing circuitry may updatethe weighting profile based on the received inputs. In some embodiments,updating the weighting profile comprises calculating an average set ofweights based on the received inputs. At 1710, the processing circuitrymay transmit the updated weighting profile to at least one of theplurality of user devices.

FIG. 18 is an illustrative display 1800 for providing attributesassociated with an entity. Display 1800 may include an identification ofthe entity 1802, an indication of the attribute 1804, and feedbackinputs 1806 and 1808. Although the display 1800 is depicted on an mobilephone interface, it will be understood that display 1800 may bedisplayed on any suitable device, including, but not limited to, mobilephones, computers, or tablets. Furthermore, it will be understood bythose of skill in the art that the attributes are not limited to the“skills & endorsements” depicted in display 1800, and that suchattributes are provided for illustrative purposes only.

Indicator 1804 may indicate an attribute associated with the entityindicated by 1802. For instance, the entity “John Doe” may be associatedwith the attribute “business analyst.” This attribute may have beenadded by the entity itself, or by the crowd. For instance, the display1800 may provide a user-selectable icon “add skill,” allowing otherentities to add attributes associated with the entity whose profile isdepicted in display 1800. The display 1800 may also includeuser-selectable icons 1806 and 1808, depicted in FIG. 18 as up and downarrows, which allow the crowd to provide feedback on the attribute. Insome embodiments, an entity may only be allowed to provide feedbackonce. That is, once the entity of the crowd has selected one of the upor down arrows (either “agreeing” or “disagreeing” with the attribute),the user-selectable icons 1806 and 1808 may deactivate and disallow theentity of the crowd from providing further feedback. In someembodiments, such as the illustrative embodiment depicted in FIG. 18,the display 1800 may provide an “add comment” selectable icon. Whenselected, this icon may allow an entity of the crowd to provide acomment on the attribute. In some embodiments, the icon may also displayhow many comments have already been left by other users of the crowd.

In some embodiments, the display 1800 may also display a net attributescore for each of the attributes 1804 listed. For example, the “businessanalyst” attribute has a net attribute score of 100 (110 “likes” minus10 “dislikes”), and this net attribute score is shown next to theindicator 1804.

FIG. 19 is an illustrative process 1900 for calculating a system trustscore based on attributes associated with an entity. Process 1900includes retrieving, from a first database, first data associated with afirst entity at 1902, calculating a first component score based on thefirst data at 1904, retrieving, from a second database, second dataassociated with the first entity at 1906, calculating a second componentscore based on the second data at 1908, calculating a weightedcombination, such as a weighted average or other suitable weightingmethod, of the first component score and the second component score toproduce a system trust score for the first entity at 1910, receiving,from a user device of a second entity, data indicating an attributeassociated with the first entity at 1912, recalculating the firstcomponent score based on the received data indicating the attribute at1914, determining whether the first component score changed by more thana threshold value at 1916, reducing the change in first component scoreto the threshold value at 1918, and updating a system trust score basedon the recalculated first component score at 1920. It will be understoodthat process 1900 depicts illustrative steps calculating a system trustscore based on attributes associated with an entity, and that one ormore of steps 1902-1920 may be omitted and additional steps added toprocess 1900 as will be apparent to those of skill in the art withoutdeparting from the scope hereof.

At 1902, processing circuitry may retrieve, from a first database, firstdata associated with a first entity at 1902. The first data may bereceived from any suitable local or remote database, such as any of thedatabases discussed in conjunction with FIG. 4 above. At 1904, theprocessing circuitry may calculate a first component score based on thefirst data. 1904 may be substantially similar to 1432 discussed inconjunction with FIG. 14 above. Similar to 1902 and 1904, the processingcircuitry may retrieve, from a second database, second data associatedwith the first entity at 1906 and calculate a second component scorebased on the second data at 1908. The second database may be a differentdatabase than the first database. Although only two component scores arediscussed in process 1900, it will be understood that any number ofcomponent scores may be calculated, and that more than one componentscore may be calculated based on data retrieved from one database. At1910, the processing circuitry may calculate a weighted combination ofthe first component score and the second component score to produce asystem trust score. 1910 may be substantially similar to steps 1436 and1438 from FIG. 14 discussed above in relation to calculating a systemtrust score.

At 1910, the processing circuitry may receive, from a user device of asecond entity, data indicating an attribute associated with the firstentity at 1912. In some embodiments, the data indicating the attributemay comprise an indication of the attribute. For example, the secondentity may provide the attribute using any suitable user interface of auser device. In some embodiments, the data indicating the attribute maycomprise feedback associated with the attribute. For example, asdiscussed above, an entity may provide feedback for an attribute throughuser-selectable icons of a user interface, such as like/dislike, thumbsup/thumbs down, a star-based system, or a numeric rating system. Thedata indicating the attribute may comprise data indicating that theentity has selected one or more of these user-selectable icons andprovided feedback for the attribute.

At 1914, the processing circuitry may recalculate the first componentscore based on the received data indicating the attribute at 1914. Asdiscussed above, attributes may be used to adjust component scoresand/or trust scores. In some embodiments, the attribute itself may causean adjustment to the component scores and/or trust scores. For instance,the fact that an entity is associated with the attribute may cause thecomponent and/or trust score to increase or decrease by a predeterminedamount (such as a number of points or a preset percentage of thecomponent or trust score). In some embodiments, feedback for theattribute left by the second entity may be used to calculate a netattribute score, and the adjustment of the component and/or trust scoremay be based on the net attribute score. For example, the processingcircuitry may calculate a difference between a number of positivefeedback and a number of negative feedback left by other entities in thecomputer network for the attribute and adjust the component scorerelated to the attribute and/or the trust score by a proportionalamount.

At 1916, the processing circuitry may optionally determine whether thefirst component score changed by more than a threshold value. In someembodiments, the processing circuitry may skip 1916 and continuedirectly to 1920. In other embodiments, the processing circuitry mayretrieve a threshold value from memory, such as local memory or remotestorage of a remote database, that indicates a threshold or maximumvalue for the component score. The threshold or maximum value may alsoindicate the maximum amount that the first component score may beadjusted based on the attribute or net attribute score. If the firstcomponent score changed by more than the threshold value, then theprocessing circuitry may reduce the change in first component score tothe threshold value at 1918 and update the system trust score based onthe recalculated first component score at 1920. If the first componentscore did not change by more than the threshold value, then theprocessing circuitry may continue directly to 1920 and update the systemtrust score based on the recalculated first component score. Updatingthe system trust score may be substantially similar to 1434 to 1438,depicted in FIG. 14. For example, updating the system trust score maycomprise receiving a set of weightings (for example, supplied either bythe user or by a system administrator) and combining the first componentscore and the second component score using a weighted combinationaccording to the set of weightings.

FIG. 20 is an illustrative process 2000 for calculating a peer trustscore based on attributes associated with an entity. Process 2000includes retrieving a system trust score of a first entity at 2001,receiving, from a user device of a second entity, data indicating anattribute associated with the first entity at 2002, receiving a requestfor the trust score for the first entity from a user device of a thirdentity at 2004, identifying a path connecting the third entity to thesecond entity at 2006, determining whether the identified path comprisesless than a threshold number of links at 2008, recalculating a componentscore based on the identified path at 2010, and calculating the peertrust score at 2012. It will be understood that process 2000 depictsillustrative steps for calculating a peer trust score based onattributes associated with an entity, and that one or more of steps2001-2014 may be omitted and additional steps added to process 2000 aswill be apparent to those of skill in the art without departing from thescope hereof.

At 2001, the processing circuitry may retrieve a system trust score of afirst entity at 2001. For example, the processing circuitry may retrievethe system trust score, which may have been calculated according toprocess 1400 or 1900, from local memory or remote memory of a remotedatabase. At 2002, the processing circuitry may receive, from a userdevice of a second entity, data indicating an attribute associated withthe first entity. 2002 may be substantially similar to 1912 describedabove in relation to FIG. 19.

At 2004, the processing circuitry may receive a request for the trustscore for the first entity from a user device of a third entity. Forinstance, the third entity (i.e., the requesting entity) may request apeer trust score for the first entity (i.e., the target entity). At2006, the processing circuitry may determine whether any of the entitiesof the “crowd,” such as the second entity, are connected to the thirdentity in a computer network. In some embodiments, this determinationcomprises identifying a path connecting the third entity to the secondentity as shown in 2006. In some embodiments, identifying the pathcomprises identifying a path from the third entity to the second entitythat has less than a threshold number of links, as shown in 2008. Inthis manner the processing circuitry may determine whether the secondentity is sufficiently related to the third entity, and whether thefeedback of the second entity on the attribute should be treated withgreater weight. If, at 2008, the processing circuitry identifies a pathcomprising less than the threshold number of links, the processingcircuitry may recalculate a component score based on the identified pathat 2010. For example, the processing circuitry may further adjust thecomponent score, either by increasing or decreasing the component score,in a similar fashion as discussed in conjunction with 1914, depicted inFIG. 19. After recalculating the component score, the processingcircuitry may proceed to 2012. If the processing circuitry cannotidentify a path from the third entity to the second entity comprisingless than a threshold number of links, then the processing circuitry mayalso proceed to 2012 without recalculating the component score. Theprocessing circuitry may calculate the peer trust score at 2012 in asimilar manner as described in relation to FIG. 15.

FIG. 21 is an illustrative process 2100 for calculating a contextualtrust score based on attributes associated with an entity. Process 2100includes retrieving a system or peer trust score of a first entity at2101, receiving, from a user device of a second entity, data indicatingan attribute associated with the first entity at 2102, receiving arequest for the trust score for the first entity from a user device of athird entity at 2104, receiving an indication of an activity to beperformed in the future by the first entity and the third entity at2106, retrieving metadata associated with the attribute at 2108,determining that the metadata indicates that the attribute is associatedwith the activity at 2110, recalculating a component score based on theattribute at 2112, and calculating a contextual trust score at 2114. Itwill be understood that process 2100 depicts illustrative steps forcalculating a contextual trust score based on attributes associated withan entity, and that one or more of steps 2101-2114 may be omitted andadditional steps added to process 2100 as will be apparent to those ofskill in the art without departing from the scope hereof.

At 2101, processing circuitry may retrieve a system or peer trust scoreof a first entity. For example, the processing circuitry may retrievethe system or peer trust score, which may have been calculated accordingto any one of the processes 1400, 1500, 1900, or 2000, from local memoryor remote memory of a remote database. At 2102, the processing circuitrymay receive, from a user device of a second entity, data indicating anattribute associated with the first entity. 2102 may be substantiallysimilar to 2002 and 1912 described above in relation to FIGS. 19 and 20.

At 2104, the processing circuitry may receive a request for the trustscore for the first entity from a user device of a third entity, and, at2106, the processing circuitry may receive an indication of an activityto be performed in the future by the first entity and the third entity.For example, the third entity may request a contextual trust score andidentify a certain activity or transaction that it is planning or wishesto perform with the first entity. At 2108, the processing circuitry mayretrieve metadata associated with the attribute. The processingcircuitry may retrieve the metadata from any suitable storage location,including local memory or remote memory of a remote database. In someembodiments, the metadata associated with the attribute may be storedwith the attribute. For example, data indicating the attribute maycomprise a header or an appended metadata that indicates informationabout the attribute, including what data the attribute might relate to,what data or types of data might automatically assign the attribute toan entity, what component scores the attribute is related to, and whattransaction/activity types the attribute is related to. In someembodiments, the metadata about the attribute may comprise data and/orinstructions for adjusting component or trust scores based on netattribute scores. In some embodiments, the metadata about the attributemay be stored separately or in separate locations as data indicating theattribute.

At 2110, the processing circuitry may determine that the metadataindicates that the attribute is associated with the activity. Forexample, the processing circuitry may search the metadata for a dataentry that indicates a relationship between the attribute and theactivity. If the activity and the attribute are related or associated,the processing circuitry may continue to 2112 and recalculate acomponent score based on the attribute. 2112 may be substantiallysimilar to 2010 discussed above in relation to FIG. 20. If the metadatadoes not indicate that the attribute is associated with the activity,then the processing circuitry may proceed to 2114 and calculate thecontextual trust score. 2114 may be substantially similar to the stepsof FIG. 16, discussed above.

FIG. 22 is an illustrative process 2200 for updating a trust score basedon extrapolated trends. Process 2200 includes retrieving a first trustscore of a first entity and a timestamp indicating a first time when thefirst trust score was calculated at 2202, determining whether adifference between the first time and a current time exceeds a thresholdat 2204, identifying a second entity in the computer network at 2206,determining that at least one trust score associated with the secondentity was calculated later than the first time at 2208, calculating atrend using trust scores associated with the second entity at 2210, andupdating the first trust score using the calculated trend at 2212. Itwill be understood that process 2200 depicts illustrative steps forupdating a trust score based on extrapolated trends, and that one ormore of steps 2202-2212 may be omitted and additional steps added toprocess 2200 as will be apparent to those of skill in the art withoutdeparting from the scope hereof.

At 2202, processing circuitry may retrieve a first trust score of afirst entity and a timestamp indicating a first time when the firsttrust score was calculated. The processing circuitry may retrieve thefirst trust score and timestamp from any suitable storage, includinglocal memory or remote memory of a remote database. In some embodiments,the first trust score and the timestamp may be stored together. Forexample, the first trust score and timestamp may be stored as the firstand second elements of an array structure. In other embodiments, thefirst trust score and the timestamp may be stored separately and/or inseparate data structures.

At 2204, the processing circuitry may determine whether a differencebetween the first time and a current time exceeds a threshold period oftime. If the difference does not exceed the threshold period of time,this may indicate that the first trust score was calculated relativelyrecently, and the processing circuitry may return to 2202 to repeat theprocess 2200 at a later time. If the difference does exceed thethreshold period of time, this may indicate that the first trust scoreis relatively outdated, and the processing circuitry may continue to2206. Although the method of updating a trust score for an entity basedon trends in trust scores of other entities is described in relation toa determination that the first trust score is relatively “outdated,” itwill be understood that this method of updating trust scores may beapplied even when the first trust score is not outdated. For instance,in some embodiments, step 2204 may be optional, and the processingcircuitry may proceed to adjust the first trust score based on trends intrust scores of other entities.

At 2206, the processing circuitry may identify a second entity in thecomputer network. In some embodiments, the processing circuitry mayidentify the second entity by identifying a path from the first entityto the second entity. The processing circuitry may identify a path fromthe first entity to the second entity that comprises fewer than athreshold number of links. In some embodiments, the processing circuitrymay choose the second entity randomly from a plurality of entities. Instill other embodiments, the processing circuitry may identify aplurality of entities. At 2208, the processing circuitry may determinethat at least one trust score associated with the second entity wascalculated later than the first time. For example the processingcircuitry may retrieve at least one trust score associated with thesecond entity (e.g., from local memory or remote memory) and a timestamp that indicates a time that the at least one trust score wascalculated. The processing circuitry may then compare the time stamp tothe time stamp for the first trust score to determine which trust scorewas calculated later. In some embodiments, 2208 may be optional, and theprocessing circuitry may continue to 2210 without performing 2208. Insuch embodiments, the processing circuitry may update the first trustscore based on trends of trust scores associated with the second entity,irrespective of when the respective trust scores were calculated.

At 2210, the processing circuitry may calculate a trend using trustscores associated with the second entity. The trend may comprise alinear regression between two data points (such as a (trust score, time)coordinate), polynomial regression, or any other type of patternmatching suitable for two or more data points, as will be understood bythose of skill in the art. As an illustrative example, the processingcircuitry may retrieve at least two trust scores associated with thesecond entity and corresponding timestamps for when the at least twotrust scores were calculated. The processing circuitry may calculate adifference between two trust scores and a difference in theircalculation times. By dividing the difference of the two trust scores bythe difference in their calculation times, the processing circuitry mayproduce a slope of increase or decrease of the trust score. In someembodiments, the processing circuitry may only receive trust scoresassociated with the second entity that are associated with calculationtimes that are later than the calculation time of the first trust scorefor the first entity. Therefore, the processing circuitry may calculatea trend in trust scores for the second entity that is relevant to thetime period between the first time and the current time.

In some embodiments, 2210 may comprise determining a trend in one ormore component scores. For example, as discussed in detail throughout, atrust score may comprise a weighted sum of a plurality of componentscores. At 2210, the processing circuitry may retrieve at least twotrust scores associated with the second entity as well as theirrespective component scores. The processing circuitry may then analyzecorresponding components scores from the at least two trust scores toidentify trends in the individual component scores. The trends may bedetermined in much the same manner as described above, including, forexample, linear regression and/or polynomial regression techniques.

At 2212, the processing circuitry may update the first trust score usingthe calculated trend. For example, the processing circuitry may applythe determined increasing or decreasing slope of trust scores to thefirst trust score. In some embodiments, updating the first trust scorecomprises finding a time difference between the first time and thecurrent time, multiplying this time difference by the slope, and addingthe resulting product to the first trust score. In some embodiments,updating the first trust score comprises extrapolating a polynomial fromthe first time to the current time using the first trust score as aninitial coordinate. In some embodiments, updating the first trust scorecomprises decomposing the first trust score into individual componentscores, applying trends in the individual component scores (derived froman analysis of the second entity's component scores) to thecorresponding component scores, and recalculating the trust score.

Although FIG. 22 is described in relation to analyzing the trends of thetrust scores/component scores of only a single entity (i.e., the secondentity), it will be understood by those of ordinary skill in the artthat trends for multiple entities may be tracked in parallel andaveraged together to produce an average trend. This average trend may beapplied according to the process 2200 depicted in FIG. 22, mutatismutandis.

The foregoing is merely illustrative of the principles of thedisclosure, and the systems, devices, and methods described herein arepresented for purposes of illustration, and not of limitation.Variations and modifications will occur to those of skill in the artafter reviewing this disclosure. The disclosed features may beimplemented, in any combination and subcombination (including multipledependent combinations and subcombinations), with one or more otherfeatures described herein. The various features described or illustratedabove, including any components thereof, may be combined or integratedin other systems. Moreover, certain features may be omitted or notimplemented. Examples, changes, substitutions, and alterationsascertainable by one skilled in the art can be made without departingfrom the scope of the information disclosed herein.

What is claimed is:
 1. A method, comprising: retrieving, by a systemcomprising a processor, first data associated with a first entity from afirst database; determining, by the system, a first component scorebased on the first data; retrieving, by the system, second dataassociated with the first entity from a second database; determining, bythe system a second component score based on the second data;determining, by the system, a first trust score for the first entitybased on a weighted combination of the first component core and thesecond component score; receiving, by the system from a deviceassociated with a second entity, attribute data indicating an attributeassociated with the first entity; updating, by the system, the firstcomponent score based on the attribute data, resulting in an updatedfirst component score; and updating, by the system, the first trustscore for the first entity based on the updated first component score,resulting in a second trust score.